Udacity - Machine Learning Engineer Nanodegree nd009t v1.0.0 - 磁力管家

BT种子名称

分享给好友

BT种子基本信息

种子哈希：e2464588d0d4fdc4e97e258f1680205f1598e05e
文档大小：5.8 GB
文档个数：4260个文档
下载次数：2905次
下载速度：极快
收录时间：2018-11-29
最近下载：2025-05-14
DMCA/屏蔽：DMCA/屏蔽

下载磁力链接

magnet:?xt=urn:btih:E2464588D0D4FDC4E97E258F1680205F1598E05E
复制磁力链接到utorrent、Bitcomet、迅雷、115、百度网盘等下载工具进行下载。

下载BT种子

磁力链接种子下载迅雷下载二维码迅雷看看
磁力链接在线播放？试试百度云网盘离线下载播放！

Udacity - Machine Learning Engineer Nanodegree nd009t v1.0.0的二维码

文档列表

Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.en.vtt 104 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.pt-BR.vtt 105 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.zh-CN.vtt 107 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.en.vtt 108 Bytes
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.pt-BR.vtt 109 Bytes
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.en.vtt 109 Bytes
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.zh-CN.vtt 113 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.ar.vtt 118 Bytes
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.ar.vtt 122 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.pt-BR.vtt 124 Bytes
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.zh-CN.vtt 125 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.zh-CN.vtt 125 Bytes
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.en.vtt 138 Bytes
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.en.vtt 139 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.ar.vtt 140 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.en.vtt 140 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.en.vtt 141 Bytes
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.pt-BR.vtt 141 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.pt-BR.vtt 143 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.pt-BR.vtt 164 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.en.vtt 164 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.zh-CN.vtt 165 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.zh-CN.vtt 166 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.zh-CN.vtt 166 Bytes
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.zh-CN.vtt 167 Bytes
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.ar.vtt 168 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.ar.vtt 171 Bytes
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.pt-BR.vtt 171 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.pt-BR.vtt 180 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.pt-BR.vtt 186 Bytes
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.ar.vtt 203 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.ar.vtt 204 Bytes
Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.pt-BR.vtt 204 Bytes
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.en.vtt 205 Bytes
Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.zh-CN.vtt 206 Bytes
Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.en.vtt 207 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.en.vtt 208 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.en.vtt 214 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.zh-CN.vtt 222 Bytes
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.pt-BR.vtt 226 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.ar.vtt 226 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.en.vtt 229 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.pt-BR.vtt 230 Bytes
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.zh-CN.vtt 232 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.pt-BR.vtt 233 Bytes
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.zh-CN.vtt 243 Bytes
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.zh-CN.vtt 245 Bytes
Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.ar.vtt 258 Bytes
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.pt-BR.vtt 271 Bytes
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.en.vtt 273 Bytes
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.zh-CN.vtt 277 Bytes
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.zh-CN.vtt 277 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.ar.vtt 282 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.ar.vtt 284 Bytes
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.pt-BR.vtt 292 Bytes
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.en.vtt 292 Bytes
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.en.vtt 298 Bytes
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.zh-CN.vtt 299 Bytes
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.ar.vtt 301 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.zh-CN.vtt 301 Bytes
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.pt-BR.vtt 302 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.en.vtt 303 Bytes
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.zh-CN.vtt 305 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.ar.vtt 306 Bytes
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.pt-BR.vtt 306 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.en-US.vtt 309 Bytes
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.en.vtt 312 Bytes
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.en.vtt 315 Bytes
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.zh-CN.vtt 316 Bytes
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.en.vtt 320 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.pt-BR.vtt 324 Bytes
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.en.vtt 325 Bytes
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.en.vtt 325 Bytes
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.pt-BR.vtt 326 Bytes
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.pt-BR.vtt 326 Bytes
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.pt-BR.vtt 331 Bytes
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.zh-CN.vtt 332 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.zh-CN.vtt 335 Bytes
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.zh-CN.vtt 342 Bytes
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.zh-CN.vtt 355 Bytes
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.en.vtt 355 Bytes
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.ar.vtt 357 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.zh-CN.vtt 357 Bytes
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.ar.vtt 359 Bytes
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.ar.vtt 360 Bytes
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.zh-CN.vtt 361 Bytes
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.zh-CN.vtt 361 Bytes
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.pt-BR.vtt 362 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 Bytes
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 Bytes
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.en.vtt 368 Bytes
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.pt-BR.vtt 369 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.zh-CN.vtt 369 Bytes
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.pt-BR.vtt 370 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.en.vtt 371 Bytes
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.zh-CN.vtt 385 Bytes
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.ar.vtt 385 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 Bytes
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.zh-CN.vtt 392 Bytes
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.ar.vtt 393 Bytes
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.en.vtt 395 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.zh-CN.vtt 396 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.en.vtt 399 Bytes
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.pt-BR.vtt 402 Bytes
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.en.vtt 406 Bytes
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.zh-CN.vtt 408 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.zh-CN.vtt 410 Bytes
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.pt-BR.vtt 410 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.en.vtt 418 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.en.vtt 419 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.en.vtt 419 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.zh-CN.vtt 419 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.pt-BR.vtt 420 Bytes
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.pt-BR.vtt 421 Bytes
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.zh-CN.vtt 422 Bytes
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.pt-BR.vtt 423 Bytes
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.zh-CN.vtt 424 Bytes
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.ar.vtt 425 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.zh-CN.vtt 425 Bytes
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.ar.vtt 425 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.pt-BR.vtt 426 Bytes
Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.zh-CN.vtt 432 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.en.vtt 435 Bytes
Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.zh-CN.vtt 437 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.zh-CN.vtt 438 Bytes
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.pt-BR.vtt 439 Bytes
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.zh-CN.vtt 440 Bytes
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.ar.vtt 444 Bytes
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.en.vtt 451 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.pt-BR.vtt 453 Bytes
README.txt 454 Bytes
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.pt-BR.vtt 454 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.pt-BR.vtt 454 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.zh-CN.vtt 456 Bytes
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.en.vtt 457 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.pt-BR.vtt 457 Bytes
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.en.vtt 458 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.pt-BR.vtt 460 Bytes
Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.pt-BR.vtt 465 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.en.vtt 466 Bytes
Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.zh-CN.vtt 467 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.zh-CN.vtt 468 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.pt-BR.vtt 472 Bytes
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.en.vtt 472 Bytes
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.en.vtt 473 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.zh-CN.vtt 473 Bytes
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.pt-BR.vtt 474 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.zh-CN.vtt 475 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.en.vtt 476 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.ja-JP.vtt 477 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.pt-BR.vtt 478 Bytes
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.ar.vtt 479 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt 481 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.pt-BR.vtt 482 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.en.vtt 483 Bytes
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.zh-CN.vtt 485 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.zh-CN.vtt 487 Bytes
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.zh-CN.vtt 488 Bytes
Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.zh-CN.vtt 488 Bytes
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.en.vtt 489 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.ar.vtt 490 Bytes
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.ar.vtt 490 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.en.vtt 495 Bytes
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.pt-BR.vtt 497 Bytes
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.zh-CN.vtt 498 Bytes
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.zh-CN.vtt 499 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.en.vtt 501 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt 501 Bytes
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.ar.vtt 505 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.en.vtt 505 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.zh-CN.vtt 507 Bytes
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.pt-BR.vtt 507 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.en.vtt 508 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.en.vtt 510 Bytes
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.ar.vtt 510 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.ar.vtt 512 Bytes
Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.pt-BR.vtt 512 Bytes
Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.en.vtt 514 Bytes
Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.en.vtt 514 Bytes
Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.en.vtt 517 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.pt-BR.vtt 518 Bytes
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.ar.vtt 521 Bytes
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.zh-CN.vtt 524 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.en.vtt 526 Bytes
Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.zh-CN.vtt 530 Bytes
Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.pt-BR.vtt 533 Bytes
Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.zh-CN.vtt 535 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.pt-BR.vtt 538 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.pt-BR.vtt 538 Bytes
Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.pt-BR.vtt 540 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.zh-CN.vtt 540 Bytes
Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.en.vtt 540 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.ar.vtt 542 Bytes
Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.pt-BR.vtt 543 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt 545 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt 548 Bytes
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.pt-BR.vtt 549 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt 551 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.pt-BR.vtt 551 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.zh-CN.vtt 555 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.zh-CN.vtt 556 Bytes
Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.zh-CN.vtt 557 Bytes
Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.en.vtt 558 Bytes
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.ar.vtt 559 Bytes
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.zh-CN.vtt 560 Bytes
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.zh-CN.vtt 561 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.ar.vtt 561 Bytes
Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.zh-CN.vtt 568 Bytes
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.ar.vtt 570 Bytes
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.pt-BR.vtt 573 Bytes
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.en.vtt 573 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.pt-BR.vtt 574 Bytes
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.en.vtt 579 Bytes
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.zh-CN.vtt 580 Bytes
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.ar.vtt 583 Bytes
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.zh-CN.vtt 584 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt 584 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.en.vtt 586 Bytes
Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.zh-CN.vtt 588 Bytes
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.zh-CN.vtt 589 Bytes
Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.zh-CN.vtt 590 Bytes
Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.pt-BR.vtt 590 Bytes
Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.pt-BR.vtt 592 Bytes
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.zh-CN.vtt 593 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.en.vtt 594 Bytes
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.en.vtt 595 Bytes
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.en.vtt 596 Bytes
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.ar.vtt 597 Bytes
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.pt-BR.vtt 599 Bytes
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.pt-BR.vtt 599 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt 600 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.en.vtt 600 Bytes
Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.en.vtt 601 Bytes
Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.en.vtt 601 Bytes
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.pt-BR.vtt 606 Bytes
Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.en.vtt 607 Bytes
Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.zh-CN.vtt 607 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt 607 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.ar.vtt 608 Bytes
Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.en-US.vtt 608 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.zh-CN.vtt 612 Bytes
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.en.vtt 613 Bytes
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.zh-CN.vtt 615 Bytes
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.pt-BR.vtt 618 Bytes
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.en.vtt 622 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.ar.vtt 624 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt 624 Bytes
Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.zh-CN.vtt 631 Bytes
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.zh-CN.vtt 633 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.en.vtt 633 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.en.vtt 634 Bytes
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.en.vtt 635 Bytes
Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.pt-BR.vtt 638 Bytes
Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.en-US.vtt 638 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.pt-BR.vtt 643 Bytes
Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.pt-BR.vtt 643 Bytes
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.en.vtt 644 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.zh-CN.vtt 655 Bytes
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.pt-BR.vtt 655 Bytes
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.pt.vtt 656 Bytes
Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.pt-BR.vtt 657 Bytes
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.zh-CN.vtt 662 Bytes
Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.en.vtt 663 Bytes
Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.zh-CN.vtt 663 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.pt-BR.vtt 663 Bytes
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.en.vtt 665 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.zh-CN.vtt 668 Bytes
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.pt-BR.vtt 671 Bytes
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.pt-BR.vtt 672 Bytes
Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.zh-CN.vtt 675 Bytes
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.zh-CN.vtt 677 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.pt-BR.vtt 678 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.ar.vtt 678 Bytes
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.zh-CN.vtt 680 Bytes
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.ar.vtt 682 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.pt-BR.vtt 683 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.pt-BR.vtt 683 Bytes
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.en.vtt 685 Bytes
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.en.vtt 688 Bytes
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.pt-BR.vtt 688 Bytes
Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.pt-BR.vtt 690 Bytes
Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.zh-CN.vtt 692 Bytes
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.ar.vtt 694 Bytes
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.en.vtt 694 Bytes
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.pt-BR.vtt 694 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.ar.vtt 697 Bytes
Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.en.vtt 701 Bytes
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.zh-CN.vtt 701 Bytes
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.en.vtt 702 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.pt-BR.vtt 705 Bytes
Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.en.vtt 707 Bytes
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.pt-BR.vtt 707 Bytes
Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.pt-BR.vtt 707 Bytes
Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.es-MX.vtt 707 Bytes
Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.zh-CN.vtt 709 Bytes
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.ar.vtt 711 Bytes
Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.en.vtt 716 Bytes
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.en-US.vtt 716 Bytes
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.pt-BR.vtt 716 Bytes
Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.zh-CN.vtt 718 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt 719 Bytes
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.zh-CN.vtt 720 Bytes
Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.pt-BR.vtt 723 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.zh-CN.vtt 723 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.en.vtt 725 Bytes
Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.zh-CN.vtt 727 Bytes
Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.pt-BR.vtt 727 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.zh-CN.vtt 729 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.pt-BR.vtt 730 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.zh-CN.vtt 733 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.zh-CN.vtt 734 Bytes
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.pt-BR.vtt 736 Bytes
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.pt-BR.vtt 737 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.zh-CN.vtt 739 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.en.vtt 739 Bytes
Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.zh-CN.vtt 742 Bytes
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.zh-CN.vtt 744 Bytes
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.ar.vtt 745 Bytes
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.en.vtt 747 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.pt-BR.vtt 754 Bytes
Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.zh-CN.vtt 756 Bytes
Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.pt-BR.vtt 760 Bytes
Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.en-US.vtt 764 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.zh-CN.vtt 766 Bytes
Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.en.vtt 767 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.ar.vtt 768 Bytes
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.en.vtt 768 Bytes
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.ar.vtt 769 Bytes
Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.pt-BR.vtt 769 Bytes
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.zh-CN.vtt 769 Bytes
Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.en-US.vtt 770 Bytes
Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.en.vtt 771 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.pt-BR.vtt 772 Bytes
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.en.vtt 772 Bytes
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.pt-BR.vtt 773 Bytes
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.en.vtt 775 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.en.vtt 777 Bytes
Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.zh-CN.vtt 777 Bytes
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.pt-BR.vtt 781 Bytes
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.ar.vtt 784 Bytes
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.pt-BR.vtt 786 Bytes
Part 06-Module 02-Lesson 03_Policy-Based Methods/01. M2L3 01 V1-YOSREyp04HA.zh-CN.vtt 787 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.en.vtt 790 Bytes
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.en.vtt 791 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.en.vtt 791 Bytes
Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.pt-BR.vtt 793 Bytes
Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.en-US.vtt 793 Bytes
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.en.vtt 797 Bytes
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.zh-CN.vtt 801 Bytes
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.en.vtt 804 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.pt-BR.vtt 804 Bytes
Part 06-Module 02-Lesson 03_Policy-Based Methods/06. M2L3 06 V1-RMjdQkl6CqE.zh-CN.vtt 804 Bytes
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.zh-CN.vtt 806 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.zh-CN.vtt 810 Bytes
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.zh-CN.vtt 810 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.ar.vtt 812 Bytes
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.zh-CN.vtt 812 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt 813 Bytes
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.zh-CN.vtt 814 Bytes
Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.pt-BR.vtt 817 Bytes
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.ar.vtt 820 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.en.vtt 820 Bytes
Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.zh-CN.vtt 822 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.zh-CN.vtt 822 Bytes
Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.pt-BR.vtt 823 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.zh-CN.vtt 823 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.en.vtt 824 Bytes
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.pt-BR.vtt 826 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.en.vtt 828 Bytes
Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.en.vtt 830 Bytes
Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.en.vtt 831 Bytes
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.es-MX.vtt 832 Bytes
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.ar.vtt 836 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.zh-CN.vtt 840 Bytes
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.ar.vtt 841 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.ar.vtt 842 Bytes
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.en.vtt 842 Bytes
Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.en-US.vtt 845 Bytes
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.zh-CN.vtt 849 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.en.vtt 850 Bytes
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.pt-BR.vtt 853 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.en.vtt 853 Bytes
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.en.vtt 855 Bytes
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.pt-BR.vtt 856 Bytes
Part 06-Module 02-Lesson 03_Policy-Based Methods/01. M2L3 01 V1-YOSREyp04HA.en.vtt 856 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.pt-BR.vtt 857 Bytes
Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.en.vtt 857 Bytes
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.zh-CN.vtt 862 Bytes
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.pt-BR.vtt 862 Bytes
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.ar.vtt 865 Bytes
Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.pt-BR.vtt 866 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.en.vtt 867 Bytes
Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.pt-BR.vtt 874 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.en.vtt 874 Bytes
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.en.vtt 879 Bytes
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.zh-CN.vtt 879 Bytes
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.pt-BR.vtt 880 Bytes
Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.ar.vtt 882 Bytes
Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.zh-CN.vtt 883 Bytes
Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion-Matrix-Solution-ywwSzyU9rYs.pt-BR.vtt 889 Bytes
Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.zh-CN.vtt 891 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.pt-BR.vtt 891 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.pt-BR.vtt 893 Bytes
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.pt-BR.vtt 895 Bytes
Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.pt-BR.vtt 895 Bytes
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.en.vtt 896 Bytes
Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.en.vtt 897 Bytes
Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.zh-CN.vtt 900 Bytes
Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.en-US.vtt 900 Bytes
Part 06-Module 02-Lesson 03_Policy-Based Methods/06. M2L3 06 V1-RMjdQkl6CqE.en.vtt 910 Bytes
Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.zh-CN.vtt 916 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.en.vtt 918 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/img/codecogseqn-58.gif 919 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. 06 Image Challenge V3-Efnoj1KNPHw.zh-CN.vtt 920 Bytes
Part 02-Module 01-Lesson 01_Training and Testing Models/01. 01 Intro-4C4PuJANIdE.zh-CN.vtt 922 Bytes
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.zh-CN.vtt 924 Bytes
Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.zh-CN.vtt 927 Bytes
Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.pt-BR.vtt 928 Bytes
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.pt-BR.vtt 928 Bytes
Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-2HY0Yr5FRn0.zh-CN.vtt 930 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. 06 Image Challenge V3-Efnoj1KNPHw.pt-BR.vtt 937 Bytes
Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.en.vtt 937 Bytes
Part 04-Module 06-Lesson 01_Random Projection and ICA/07. L6 5 ICA Implementation V1 V1-fZGxYfJmKaE.en.vtt 938 Bytes
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.ar.vtt 938 Bytes
Part 03-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.en.vtt 939 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.en.vtt 943 Bytes
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.en.vtt 943 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions Cont.-utOv-BKI_vo.zh-CN.vtt 944 Bytes
Part 02-Module 01-Lesson 01_Training and Testing Models/01. 01 Intro-4C4PuJANIdE.pt-BR.vtt 945 Bytes
Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.pt-BR.vtt 947 Bytes
Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.pt-BR.vtt 950 Bytes
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.pt-BR.vtt 954 Bytes
Part 04-Module 06-Lesson 01_Random Projection and ICA/07. L6 5 ICA Implementation V1 V1-fZGxYfJmKaE.pt-BR.vtt 955 Bytes
Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.pt-BR.vtt 956 Bytes
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.en.vtt 957 Bytes
Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion-Matrix-Solution-ywwSzyU9rYs.zh-CN.vtt 959 Bytes
Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.pt-BR.vtt 959 Bytes
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.en-US.vtt 960 Bytes
Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.zh-CN.vtt 965 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.zh-CN.vtt 965 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions Cont.-utOv-BKI_vo.pt-BR.vtt 965 Bytes
Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.pt-BR.vtt 966 Bytes
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.zh-CN.vtt 969 Bytes
Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.pt-BR.vtt 970 Bytes
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.pt-BR.vtt 975 Bytes
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.ar.vtt 976 Bytes
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.pt-BR.vtt 977 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training The Neural Network-HwiI-UXUx-M.pt-BR.vtt 977 Bytes
Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.en.vtt 983 Bytes
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.en.vtt 984 Bytes
Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-2HY0Yr5FRn0.en.vtt 989 Bytes
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.en.vtt 991 Bytes
Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.pt-BR.vtt 993 Bytes
Part 02-Module 01-Lesson 01_Training and Testing Models/01. 01 Intro-4C4PuJANIdE.en.vtt 994 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training The Neural Network-HwiI-UXUx-M.zh-CN.vtt 995 Bytes
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.zh-CN.vtt 996 Bytes
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.ar.vtt 999 Bytes
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.en.vtt 1.0 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.zh-CN.vtt 1.0 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.en-US.vtt 1.0 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.zh-CN.vtt 1.0 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.pt-BR.vtt 1.0 kB
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.ar.vtt 1.0 kB
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.ar.vtt 1.0 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.zh-CN.vtt 1.0 kB
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.ar.vtt 1.0 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.zh-CN.vtt 1.0 kB
Part 05-Module 01-Lesson 01_Neural Networks/08. XOR Perceptron-TF83GfjYLdw.zh-CN.vtt 1.0 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. XOR Perceptron-TF83GfjYLdw.zh-CN.vtt 1.0 kB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.pt-BR.vtt 1.0 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. XOR Perceptron-TF83GfjYLdw.pt-BR.vtt 1.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.pt-BR.vtt 1.0 kB
Part 05-Module 01-Lesson 01_Neural Networks/08. XOR Perceptron-TF83GfjYLdw.pt-BR.vtt 1.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.en.vtt 1.0 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.zh-CN.vtt 1.0 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.en.vtt 1.0 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. XOR Perceptron-TF83GfjYLdw.en.vtt 1.0 kB
Part 05-Module 01-Lesson 01_Neural Networks/08. XOR Perceptron-TF83GfjYLdw.en.vtt 1.0 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.en.vtt 1.0 kB
Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.zh-CN.vtt 1.0 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/15. Local Minima-gF_sW_nY-xw.zh-CN.vtt 1.0 kB
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.zh-CN.vtt 1.0 kB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.pt-BR.vtt 1.0 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.zh-CN.vtt 1.0 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.pt-BR.vtt 1.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.pt-BR.vtt 1.0 kB
Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.es-MX.vtt 1.0 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.zh-CN.vtt 1.0 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa-kEKupCyU0P0.zh-CN.vtt 1.0 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.pt-BR.vtt 1.0 kB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.en.vtt 1.0 kB
Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.en.vtt 1.0 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions Cont.-utOv-BKI_vo.en.vtt 1.1 kB
Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.en-US.vtt 1.1 kB
Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.pt-BR.vtt 1.1 kB
Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.pt-BR.vtt 1.1 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/gif-1.gif 1.1 kB
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.zh-CN.vtt 1.1 kB
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.ar.vtt 1.1 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.en.vtt 1.1 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.en.vtt 1.1 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2-8nG8zzJMbZw. 2 RENDER-8nG8zzJMbZw.pt-BR.vtt 1.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/15. Local Minima-gF_sW_nY-xw.pt-BR.vtt 1.1 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion-Matrix-Solution-ywwSzyU9rYs.en.vtt 1.1 kB
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.ar.vtt 1.1 kB
Part 03-Module 01-Lesson 01_Linear Regression/05. Moving A Line-8EIHFyL2Log.pt-BR.vtt 1.1 kB
Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.en.vtt 1.1 kB
Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.en.vtt 1.1 kB
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.zh-CN.vtt 1.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees Question-1RonLycEJ34.zh-CN.vtt 1.1 kB
Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.en-US.vtt 1.1 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.en.vtt 1.1 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.pt-BR.vtt 1.1 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.en-US.vtt 1.1 kB
Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.pt-BR.vtt 1.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.zh-CN.vtt 1.1 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. 06 Image Challenge V3-Efnoj1KNPHw.en.vtt 1.1 kB
Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.zh-CN.vtt 1.1 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.pt-BR.vtt 1.1 kB
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.zh-CN.vtt 1.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.pt-BR.vtt 1.1 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.pt-BR.vtt 1.1 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.pt-BR.vtt 1.1 kB
Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.zh-CN.vtt 1.1 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.pt-BR.vtt 1.1 kB
Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.zh-CN.vtt 1.1 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.zh-CN.vtt 1.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees Question-1RonLycEJ34.pt-BR.vtt 1.1 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2-8nG8zzJMbZw. 2 RENDER-8nG8zzJMbZw.zh-CN.vtt 1.1 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion-Matrix-Solution-ywwSzyU9rYs.en-US.vtt 1.1 kB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.zh-CN.vtt 1.1 kB
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.ar.vtt 1.1 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training The Neural Network-HwiI-UXUx-M.en.vtt 1.1 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.en.vtt 1.1 kB
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.zh-CN.vtt 1.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.en.vtt 1.1 kB
Part 03-Module 01-Lesson 04_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.zh-CN.vtt 1.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.zh-CN.vtt 1.1 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.zh-CN.vtt 1.2 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.pt-BR.vtt 1.2 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/f4.gif 1.2 kB
Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.pt-BR.vtt 1.2 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.en.vtt 1.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/15. Local Minima-gF_sW_nY-xw.en.vtt 1.2 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.pt-BR.vtt 1.2 kB
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.zh-CN.vtt 1.2 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.zh-CN.vtt 1.2 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees Question-1RonLycEJ34.en.vtt 1.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.en.vtt 1.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.zh-CN.vtt 1.2 kB
Part 03-Module 01-Lesson 01_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.en.vtt 1.2 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.zh-CN.vtt 1.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.pt-BR.vtt 1.2 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.en.vtt 1.2 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/05. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.pt-BR.vtt 1.2 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.en-US.vtt 1.2 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.zh-CN.vtt 1.2 kB
Part 03-Module 01-Lesson 01_Linear Regression/05. Moving A Line-8EIHFyL2Log.en.vtt 1.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.en.vtt 1.2 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.en.vtt 1.2 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.pt-BR.vtt 1.2 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.zh-CN.vtt 1.2 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.pt-BR.vtt 1.2 kB
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.en.vtt 1.2 kB
Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.zh-CN.vtt 1.2 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.pt-BR.vtt 1.2 kB
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.ar.vtt 1.2 kB
Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.zh-CN.vtt 1.2 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/e.gif 1.2 kB
Part 03-Module 01-Lesson 01_Linear Regression/21. Polynomial Regression-DBhWG-PagEQ.pt-BR.vtt 1.2 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.pt-BR.vtt 1.2 kB
Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.en.vtt 1.2 kB
Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.en-US.vtt 1.2 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.en.vtt 1.2 kB
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.pt-BR.vtt 1.2 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.zh-CN.vtt 1.2 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.en.vtt 1.2 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.en.vtt 1.2 kB
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.en.vtt 1.2 kB
Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.en.vtt 1.2 kB
Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.en-US.vtt 1.2 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.zh-CN.vtt 1.2 kB
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.zh-CN.vtt 1.2 kB
Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.es-MX.vtt 1.2 kB
Part 03-Module 01-Lesson 01_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.pt-BR.vtt 1.2 kB
Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.zh-CN.vtt 1.2 kB
Part 03-Module 01-Lesson 04_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.pt-BR.vtt 1.2 kB
Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.pt-BR.vtt 1.2 kB
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.ar.vtt 1.2 kB
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.en.vtt 1.3 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.en.vtt 1.3 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa-kEKupCyU0P0.en.vtt 1.3 kB
Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.pt-BR.vtt 1.3 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/linear-equation.gif 1.3 kB
Part 03-Module 01-Lesson 04_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.en.vtt 1.3 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2-8nG8zzJMbZw. 2 RENDER-8nG8zzJMbZw.en-US.vtt 1.3 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.zh-CN.vtt 1.3 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.zh-CN.vtt 1.3 kB
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.en.vtt 1.3 kB
Part 10-Module 02-Lesson 05_Trees/13. BST Complications-pcB0wV7myy4.pt-BR.vtt 1.3 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.zh-CN.vtt 1.3 kB
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.ar.vtt 1.3 kB
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.pt-BR.vtt 1.3 kB
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.zh-CN.vtt 1.3 kB
Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.zh-CN.vtt 1.3 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.en.vtt 1.3 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/05. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.en.vtt 1.3 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.pt-BR.vtt 1.3 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.en-US.vtt 1.3 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.en.vtt 1.3 kB
Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.pt-BR.vtt 1.3 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.zh-CN.vtt 1.3 kB
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.en.vtt 1.3 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.en.vtt 1.3 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.pt-BR.vtt 1.3 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.zh-CN.vtt 1.3 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.zh-CN.vtt 1.3 kB
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.pt-BR.vtt 1.3 kB
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.pt-BR.vtt 1.3 kB
Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.zh-CN.vtt 1.3 kB
Part 03-Module 01-Lesson 01_Linear Regression/21. Polynomial Regression-DBhWG-PagEQ.en.vtt 1.3 kB
Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.en.vtt 1.3 kB
Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.zh-CN.vtt 1.3 kB
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.pt-BR.vtt 1.3 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.en.vtt 1.3 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.pt-BR.vtt 1.3 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.pt-BR.vtt 1.3 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.zh-CN.vtt 1.3 kB
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.zh-CN.vtt 1.3 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.pt-BR.vtt 1.3 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/codecogseqn-62.gif 1.3 kB
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.ar.vtt 1.3 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.en.vtt 1.3 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.en-US.vtt 1.3 kB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.en.vtt 1.3 kB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.ar.vtt 1.3 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.zh-CN.vtt 1.3 kB
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.zh-CN.vtt 1.4 kB
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.ar.vtt 1.4 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.en.vtt 1.4 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.en.vtt 1.4 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.zh-CN.vtt 1.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.en.vtt 1.4 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.pt-BR.vtt 1.4 kB
Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.en.vtt 1.4 kB
Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.zh-CN.vtt 1.4 kB
Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.zh-CN.vtt 1.4 kB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.ar.vtt 1.4 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.pt-BR.vtt 1.4 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.pt-BR.vtt 1.4 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.pt-BR.vtt 1.4 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.en.vtt 1.4 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.zh-CN.vtt 1.4 kB
Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.en.vtt 1.4 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.zh-CN.vtt 1.4 kB
Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.en.vtt 1.4 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.pt-BR.vtt 1.4 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.en.vtt 1.4 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.pt-BR.vtt 1.4 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.zh-CN.vtt 1.4 kB
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.zh-CN.vtt 1.4 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.zh-CN.vtt 1.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.en.vtt 1.4 kB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.pt-BR.vtt 1.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.pt-BR.vtt 1.4 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.zh-CN.vtt 1.4 kB
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.en.vtt 1.4 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/02. 02 Intro SC V1-mIgABrjJVBY.pt-BR.vtt 1.4 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.pt-BR.vtt 1.4 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.en.vtt 1.4 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Mat HS-9P7UPWFu8w8.zh-CN.vtt 1.4 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.zh-CN.vtt 1.4 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/y.gif 1.4 kB
Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.zh-CN.vtt 1.4 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.zh-CN.vtt 1.4 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.zh-CN.vtt 1.4 kB
Part 03-Module 01-Lesson 01_Linear Regression/04. Fitting A Line-gkdoknEEcaI.en.vtt 1.4 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.zh-CN.vtt 1.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.zh-CN.vtt 1.5 kB
Part 03-Module 01-Lesson 01_Linear Regression/04. Fitting A Line-gkdoknEEcaI.pt-BR.vtt 1.5 kB
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.en.vtt 1.5 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.en.vtt 1.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.pt-BR.vtt 1.5 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.zh-CN.vtt 1.5 kB
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.en.vtt 1.5 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.zh-CN.vtt 1.5 kB
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.zh-CN.vtt 1.5 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.zh-CN.vtt 1.5 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.es-MX.vtt 1.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.zh-CN.vtt 1.5 kB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.zh-CN.vtt 1.5 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.en.vtt 1.5 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.en.vtt 1.5 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.en.vtt 1.5 kB
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.pt-BR.vtt 1.5 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.en-US.vtt 1.5 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/02. 02 Intro SC V1-mIgABrjJVBY.en.vtt 1.5 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.zh-CN.vtt 1.5 kB
Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.pt-BR.vtt 1.5 kB
Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.pt-BR.vtt 1.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.zh-CN.vtt 1.5 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.en.vtt 1.5 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.zh-CN.vtt 1.5 kB
Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.en.vtt 1.5 kB
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.pt-BR.vtt 1.5 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.pt-BR.vtt 1.5 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.en.vtt 1.5 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.en.vtt 1.5 kB
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.pt-BR.vtt 1.5 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.zh-CN.vtt 1.5 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.en.vtt 1.5 kB
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.en.vtt 1.5 kB
Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.es-MX.vtt 1.5 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.zh-CN.vtt 1.5 kB
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.pt-BR.vtt 1.5 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.zh-CN.vtt 1.5 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.pt-BR.vtt 1.5 kB
Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.pt-BR.vtt 1.5 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.pt-BR.vtt 1.5 kB
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.en.vtt 1.5 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.en.vtt 1.5 kB
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.en-US.vtt 1.5 kB
Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.zh-CN.vtt 1.5 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.pt-BR.vtt 1.5 kB
Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.ar.vtt 1.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.pt-BR.vtt 1.5 kB
Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.en.vtt 1.5 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.es-MX.vtt 1.6 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.pt-BR.vtt 1.6 kB
Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.zh-CN.vtt 1.6 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.pt-BR.vtt 1.6 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.pt-BR.vtt 1.6 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.en.vtt 1.6 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.en.vtt 1.6 kB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.zh-CN.vtt 1.6 kB
Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.zh-CN.vtt 1.6 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.en-US.vtt 1.6 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.en-US.vtt 1.6 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.pt-BR.vtt 1.6 kB
Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.zh-CN.vtt 1.6 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.en.vtt 1.6 kB
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.pt-BR.vtt 1.6 kB
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.pt-BR.vtt 1.6 kB
Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.pt-BR.vtt 1.6 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.pt-BR.vtt 1.6 kB
Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.zh-CN.vtt 1.6 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Mat HS-9P7UPWFu8w8.en-US.vtt 1.6 kB
Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.en.vtt 1.6 kB
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.en.vtt 1.6 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.zh-CN.vtt 1.6 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.en.vtt 1.6 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.pt-BR.vtt 1.6 kB
Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.en.vtt 1.6 kB
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.pt-BR.vtt 1.6 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.pt-BR.vtt 1.6 kB
Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.zh-CN.vtt 1.6 kB
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.ar.vtt 1.6 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.en.vtt 1.6 kB
Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.en.vtt 1.6 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.en.vtt 1.6 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.en-US.vtt 1.6 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.en.vtt 1.6 kB
Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.en.vtt 1.6 kB
Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.pt-BR.vtt 1.6 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.en.vtt 1.6 kB
Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.pt-BR.vtt 1.6 kB
Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.zh-CN.vtt 1.6 kB
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.ar.vtt 1.6 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.pt-BR.vtt 1.6 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/f6.gif 1.6 kB
Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.zh-CN.vtt 1.6 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.pt-BR.vtt 1.6 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.pt-BR.vtt 1.6 kB
Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.pt-BR.vtt 1.6 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.en.vtt 1.6 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.en.vtt 1.6 kB
Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.zh-CN.vtt 1.7 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.en.vtt 1.7 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.pt-BR.vtt 1.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.pt-BR.vtt 1.7 kB
Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.zh-CN.vtt 1.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt 1.7 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.zh-CN.vtt 1.7 kB
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.ar.vtt 1.7 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples & Applications MAIN V1 V2-HTahFoQwk2g.zh-CN.vtt 1.7 kB
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.en.vtt 1.7 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.zh-CN.vtt 1.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.en.vtt 1.7 kB
Part 02-Module 03-Lesson 01_Model Selection/12. Outro SC V1-YD1grQje9fw.en.vtt 1.7 kB
Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.zh-CN.vtt 1.7 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.zh-CN.vtt 1.7 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.zh-CN.vtt 1.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.zh-CN.vtt 1.7 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.zh-CN.vtt 1.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.en.vtt 1.7 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.en-US.vtt 1.7 kB
Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.en.vtt 1.7 kB
Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.en.vtt 1.7 kB
Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.zh-CN.vtt 1.7 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.en.vtt 1.7 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.zh-CN.vtt 1.7 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.zh-CN.vtt 1.7 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.pt-BR.vtt 1.7 kB
Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.zh-CN.vtt 1.7 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.pt-BR.vtt 1.7 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/backprop-weight-update.gif 1.7 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.en.vtt 1.7 kB
Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.pt-BR.vtt 1.7 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.zh-CN.vtt 1.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.zh-CN.vtt 1.7 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.en.vtt 1.7 kB
Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.en.vtt 1.7 kB
Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.pt-BR.vtt 1.7 kB
Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.en-US.vtt 1.7 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.en.vtt 1.7 kB
Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.pt-BR.vtt 1.7 kB
Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.pt-BR.vtt 1.7 kB
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.ar.vtt 1.7 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.pt-BR.vtt 1.7 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt 1.7 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt 1.7 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt 1.7 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.pt-BR.vtt 1.8 kB
Part 02-Module 03-Lesson 01_Model Selection/12. Outro SC V1-YD1grQje9fw.pt-BR.vtt 1.8 kB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.en.vtt 1.8 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.en.vtt 1.8 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.pt-BR.vtt 1.8 kB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.ar.vtt 1.8 kB
Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.pt-BR.vtt 1.8 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.pt-BR.vtt 1.8 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.pt-BR.vtt 1.8 kB
Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.en.vtt 1.8 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.zh-CN.vtt 1.8 kB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.pt-BR.vtt 1.8 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.pt-BR.vtt 1.8 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.zh-CN.vtt 1.8 kB
Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.zh-CN.vtt 1.8 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hidden-layer-weights.gif 1.8 kB
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.ar.vtt 1.8 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.zh-CN.vtt 1.8 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.en.vtt 1.8 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.en.vtt 1.8 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.en.vtt 1.8 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.en.vtt 1.8 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.en.vtt 1.8 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.en.vtt 1.8 kB
Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.en.vtt 1.8 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.zh-CN.vtt 1.8 kB
Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.en.vtt 1.8 kB
Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.zh-CN.vtt 1.8 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.en.vtt 1.8 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.pt-BR.vtt 1.8 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.pt-BR.vtt 1.8 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.zh-CN.vtt 1.8 kB
Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.pt-BR.vtt 1.8 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.zh-CN.vtt 1.8 kB
Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.pt-BR.vtt 1.8 kB
Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.en.vtt 1.8 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.pt-BR.vtt 1.8 kB
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.zh-CN.vtt 1.8 kB
Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.en.vtt 1.8 kB
Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.en.vtt 1.8 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt 1.8 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt 1.8 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt 1.8 kB
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.ar.vtt 1.9 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.zh-CN.vtt 1.9 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.zh-CN.vtt 1.9 kB
Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.en.vtt 1.9 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.zh-CN.vtt 1.9 kB
Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.en-US.vtt 1.9 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.en.vtt 1.9 kB
Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.zh-CN.vtt 1.9 kB
Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.en.vtt 1.9 kB
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.zh-CN.vtt 1.9 kB
Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.en.vtt 1.9 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.es-MX.vtt 1.9 kB
Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.en-US.vtt 1.9 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.zh-CN.vtt 1.9 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.ja-JP.vtt 1.9 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.en.vtt 1.9 kB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.pt-BR.vtt 1.9 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.zh-CN.vtt 1.9 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.pt-BR.vtt 1.9 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.en.vtt 1.9 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.pt-BR.vtt 1.9 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.pt-BR.vtt 1.9 kB
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.ar.vtt 1.9 kB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.en.vtt 1.9 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.zh-CN.vtt 1.9 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.pt-BR.vtt 1.9 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.pt-BR.vtt 1.9 kB
Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.zh-CN.vtt 1.9 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.pt-BR.vtt 1.9 kB
Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.pt-BR.vtt 1.9 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.zh-CN.vtt 1.9 kB
Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.pt-BR.vtt 1.9 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.pt-BR.vtt 1.9 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/f2.gif 1.9 kB
Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.zh-CN.vtt 1.9 kB
Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.en.vtt 1.9 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.pt-BR.vtt 1.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.pt-BR.vtt 1.9 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.zh-CN.vtt 1.9 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.es-MX.vtt 1.9 kB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.zh-CN.vtt 1.9 kB
Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.en.vtt 2.0 kB
Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.en-US.vtt 2.0 kB
Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.zh-CN.vtt 2.0 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.en.vtt 2.0 kB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.zh-CN.vtt 2.0 kB
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.ar.vtt 2.0 kB
Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.en.vtt 2.0 kB
Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.zh-CN.vtt 2.0 kB
Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.en.vtt 2.0 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.pt-BR.vtt 2.0 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.pt-BR.vtt 2.0 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.en.vtt 2.0 kB
Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.pt-BR.vtt 2.0 kB
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.ar.vtt 2.0 kB
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.zh-CN.vtt 2.0 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.es-MX.vtt 2.0 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.es-MX.vtt 2.0 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.es-MX.vtt 2.0 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.en.vtt 2.0 kB
Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.zh-CN.vtt 2.0 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.zh-CN.vtt 2.0 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.pt-BR.vtt 2.0 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.zh-CN.vtt 2.0 kB
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.pt-BR.vtt 2.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.en.vtt 2.0 kB
Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.zh-CN.vtt 2.0 kB
Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.zh-CN.vtt 2.0 kB
Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.pt-BR.vtt 2.0 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.pt-BR.vtt 2.0 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.pt-BR.vtt 2.0 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.pt-BR.vtt 2.0 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples & Applications MAIN V1 V2-HTahFoQwk2g.en.vtt 2.0 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.pt-BR.vtt 2.0 kB
Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.en.vtt 2.0 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.en.vtt 2.0 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.en.vtt 2.0 kB
Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.en-US.vtt 2.0 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples & Applications MAIN V1 V2-HTahFoQwk2g.pt-BR.vtt 2.0 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.en-US.vtt 2.0 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.zh-CN.vtt 2.0 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.es-MX.vtt 2.0 kB
Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.zh-CN.vtt 2.0 kB
Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.pt-BR.vtt 2.0 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.pt-BR.vtt 2.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.zh-CN.vtt 2.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.pt-BR.vtt 2.1 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.pt-BR.vtt 2.1 kB
Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.zh-CN.vtt 2.1 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/f1.gif 2.1 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.pt-BR.vtt 2.1 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.pt-BR.vtt 2.1 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.zh-CN.vtt 2.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.zh-CN.vtt 2.1 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.pt-BR.vtt 2.1 kB
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.pt-BR.vtt 2.1 kB
Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.en.vtt 2.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.pt-BR.vtt 2.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.pt-BR.vtt 2.1 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.zh-CN.vtt 2.1 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.en.vtt 2.1 kB
Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.pt-BR.vtt 2.1 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.pt-BR.vtt 2.1 kB
Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.zh-CN.vtt 2.1 kB
Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.en.vtt 2.1 kB
Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.pt-BR.vtt 2.1 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.zh-CN.vtt 2.1 kB
Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.en-US.vtt 2.1 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.zh-CN.vtt 2.1 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.en.vtt 2.1 kB
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.ar.vtt 2.1 kB
Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.zh-CN.vtt 2.1 kB
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.en.vtt 2.1 kB
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.ar.vtt 2.1 kB
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.en.vtt 2.1 kB
Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.pt-BR.vtt 2.1 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.en.vtt 2.1 kB
Part 02-Module 03-Lesson 01_Model Selection/04. KFold Cross Validation V3 V1-9W6o6eWGi-0.pt-BR.vtt 2.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.zh-CN.vtt 2.1 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/codecogseqn-61.gif 2.1 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.en.vtt 2.1 kB
Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.zh-CN.vtt 2.1 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.en.vtt 2.1 kB
Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.en.vtt 2.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.en.vtt 2.1 kB
Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.es-MX.vtt 2.1 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.zh-CN.vtt 2.1 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.zh-CN.vtt 2.1 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.zh-CN.vtt 2.1 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.en.vtt 2.1 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.en-US.vtt 2.1 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.zh-CN.vtt 2.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/img/codecogseqn-49.gif 2.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/img/sigmoid-derivative.gif 2.1 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.zh-CN.vtt 2.1 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.zh-CN.vtt 2.1 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.zh-CN.vtt 2.1 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.zh-CN.vtt 2.2 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.zh-CN.vtt 2.2 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.zh-CN.vtt 2.2 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.es-MX.vtt 2.2 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.es-MX.vtt 2.2 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.es-MX.vtt 2.2 kB
Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.zh-CN.vtt 2.2 kB
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.ar.vtt 2.2 kB
Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.zh-CN.vtt 2.2 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.zh-CN.vtt 2.2 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.zh-CN.vtt 2.2 kB
Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.pt-BR.vtt 2.2 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.zh-CN.vtt 2.2 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.zh-CN.vtt 2.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.pt-BR.vtt 2.2 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.zh-CN.vtt 2.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.pt-BR.vtt 2.2 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.en.vtt 2.2 kB
Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.pt-BR.vtt 2.2 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.zh-CN.vtt 2.2 kB
Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.ar.vtt 2.2 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.zh-CN.vtt 2.2 kB
Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.pt-BR.vtt 2.2 kB
Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.en.vtt 2.2 kB
Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.en.vtt 2.2 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.pt-BR.vtt 2.2 kB
Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.ar.vtt 2.2 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt 2.2 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt 2.2 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt 2.2 kB
Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.zh-CN.vtt 2.2 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.en.vtt 2.2 kB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.en.vtt 2.2 kB
Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.zh-CN.vtt 2.2 kB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.pt-BR.vtt 2.2 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.ar.vtt 2.2 kB
Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.zh-CN.vtt 2.2 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.en.vtt 2.2 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/backprop-general.gif 2.3 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.pt-BR.vtt 2.3 kB
Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.zh-CN.vtt 2.3 kB
Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.en.vtt 2.3 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.zh-CN.vtt 2.3 kB
Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.zh-CN.vtt 2.3 kB
Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.zh-CN.vtt 2.3 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.en.vtt 2.3 kB
Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.en.vtt 2.3 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.en.vtt 2.3 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.zh-CN.vtt 2.3 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.zh-CN.vtt 2.3 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.en.vtt 2.3 kB
Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.pt-BR.vtt 2.3 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.pt-BR.vtt 2.3 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.en-US.vtt 2.3 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.zh-CN.vtt 2.3 kB
Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.zh-CN.vtt 2.3 kB
Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.en-US.vtt 2.3 kB
Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.en.vtt 2.3 kB
Part 10-Module 02-Lesson 05_Trees/13. BST Complications-pcB0wV7myy4.zh-CN.vtt 2.3 kB
Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.en.vtt 2.3 kB
Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.zh-CN.vtt 2.3 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.pt-BR.vtt 2.3 kB
Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.pt-BR.vtt 2.3 kB
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.zh-CN.vtt 2.3 kB
Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.zh-CN.vtt 2.3 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.es-MX.vtt 2.3 kB
Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.pt-BR.vtt 2.3 kB
Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.zh-CN.vtt 2.3 kB
Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.pt-BR.vtt 2.3 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/codecogseqn-2.png 2.3 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.pt-BR.vtt 2.3 kB
Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.zh-CN.vtt 2.3 kB
Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.en.vtt 2.3 kB
Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.en-US.vtt 2.3 kB
Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.ar.vtt 2.3 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.zh-CN.vtt 2.3 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.ar.vtt 2.3 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.pt-BR.vtt 2.3 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.pt-BR.vtt 2.3 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.en.vtt 2.3 kB
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.en.vtt 2.3 kB
Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.pt-BR.vtt 2.3 kB
Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.pt-BR.vtt 2.3 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.en.vtt 2.3 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.en.vtt 2.3 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.en-US.vtt 2.3 kB
Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.en.vtt 2.3 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.en.vtt 2.3 kB
Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.en.vtt 2.4 kB
Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.en-US.vtt 2.4 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.en.vtt 2.4 kB
Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.en.vtt 2.4 kB
Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.en.vtt 2.4 kB
Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.zh-CN.vtt 2.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.zh-CN.vtt 2.4 kB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.ar.vtt 2.4 kB
Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.en-US.vtt 2.4 kB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.ar.vtt 2.4 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.zh-CN.vtt 2.4 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.zh-CN.vtt 2.4 kB
Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-r7g0Z-54vg0.en.vtt 2.4 kB
Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.pt-BR.vtt 2.4 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.en.vtt 2.4 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.en.vtt 2.4 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.en.vtt 2.4 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.en.vtt 2.4 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.pt-BR.vtt 2.4 kB
Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.zh-CN.vtt 2.4 kB
Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.pt-BR.vtt 2.4 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.en.vtt 2.4 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.zh-CN.vtt 2.4 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.zh-CN.vtt 2.4 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.zh-CN.vtt 2.4 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.zh-CN.vtt 2.4 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.zh-CN.vtt 2.4 kB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.en.vtt 2.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.zh-CN.vtt 2.4 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.zh-CN.vtt 2.4 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.en.vtt 2.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt 2.4 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt 2.4 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.en-US.vtt 2.4 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.zh-CN.vtt 2.4 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt 2.4 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.en.vtt 2.4 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt 2.4 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt 2.4 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.en.vtt 2.4 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.pt-BR.vtt 2.4 kB
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.zh-CN.vtt 2.4 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.pt-BR.vtt 2.4 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.zh-CN.vtt 2.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.zh-CN.vtt 2.4 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.pt-BR.vtt 2.4 kB
Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.en.vtt 2.4 kB
Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.pt-BR.vtt 2.4 kB
Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.en-US.vtt 2.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt 2.4 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt 2.4 kB
Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.en.vtt 2.4 kB
Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.pt-BR.vtt 2.4 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.pt-BR.vtt 2.5 kB
Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.pt-BR.vtt 2.5 kB
Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.pt-BR.vtt 2.5 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.zh-CN.vtt 2.5 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.zh-CN.vtt 2.5 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.zh-CN.vtt 2.5 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.pt-BR.vtt 2.5 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.en.vtt 2.5 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.zh-CN.vtt 2.5 kB
Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.en.vtt 2.5 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.pt-BR.vtt 2.5 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.pt-BR.vtt 2.5 kB
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.pt-BR.vtt 2.5 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt 2.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt 2.5 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.pt-BR.vtt 2.5 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.en-US.vtt 2.5 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.zh-CN.vtt 2.5 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.pt-BR.vtt 2.5 kB
Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.pt-BR.vtt 2.5 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.pt-BR.vtt 2.5 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.en.vtt 2.5 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.es-MX.vtt 2.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.zh-CN.vtt 2.5 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.zh-CN.vtt 2.5 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.en.vtt 2.5 kB
Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.en.vtt 2.5 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.zh-CN.vtt 2.5 kB
Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.en-US.vtt 2.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.pt-BR.vtt 2.5 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.en.vtt 2.5 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.zh-CN.vtt 2.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt 2.5 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.pt-BR.vtt 2.5 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt 2.5 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.en.vtt 2.5 kB
Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.en.vtt 2.5 kB
Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.pt-BR.vtt 2.5 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.pt-BR.vtt 2.5 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.en.vtt 2.5 kB
Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.en-US.vtt 2.5 kB
Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.en.vtt 2.5 kB
Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.pt-BR.vtt 2.6 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.zh-CN.vtt 2.6 kB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.pt-BR.vtt 2.6 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.en.vtt 2.6 kB
Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.zh-CN.vtt 2.6 kB
Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.pt-BR.vtt 2.6 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.en.vtt 2.6 kB
Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.en.vtt 2.6 kB
Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.pt-BR.vtt 2.6 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.en-US.vtt 2.6 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.zh-CN.vtt 2.6 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.zh-CN.vtt 2.6 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.en.vtt 2.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.pt-BR.vtt 2.6 kB
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.zh-CN.vtt 2.6 kB
Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.pt-BR.vtt 2.6 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.pt-BR.vtt 2.6 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.en.vtt 2.6 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.pt-BR.vtt 2.6 kB
Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.pt-BR.vtt 2.6 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.en-US.vtt 2.6 kB
Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.en.vtt 2.6 kB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.pt-BR.vtt 2.6 kB
Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.en-US.vtt 2.6 kB
Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.zh-CN.vtt 2.6 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.en.vtt 2.6 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.en.vtt 2.6 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.en.vtt 2.6 kB
Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.zh-CN.vtt 2.6 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt 2.6 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt 2.6 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt 2.6 kB
Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.en.vtt 2.6 kB
Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.en.vtt 2.6 kB
Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.en-US.vtt 2.6 kB
Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.zh-CN.vtt 2.6 kB
Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.pt-BR.vtt 2.6 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.pt-BR.vtt 2.6 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.en.vtt 2.6 kB
Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.zh-CN.vtt 2.6 kB
Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.pt-BR.vtt 2.6 kB
Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.ar.vtt 2.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.pt-BR.vtt 2.6 kB
Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.pt-BR.vtt 2.6 kB
Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.zh-CN.vtt 2.6 kB
Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.zh-CN.vtt 2.6 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt 2.6 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt 2.6 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt 2.6 kB
Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.en.vtt 2.6 kB
Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.en-US.vtt 2.6 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.zh-CN.vtt 2.6 kB
Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.en.vtt 2.7 kB
Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.es-MX.vtt 2.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.en.vtt 2.7 kB
Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.ar.vtt 2.7 kB
Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.pt-BR.vtt 2.7 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.zh-CN.vtt 2.7 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.es-MX.vtt 2.7 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.pt-BR.vtt 2.7 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.pt-BR.vtt 2.7 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.en-US.vtt 2.7 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.en.vtt 2.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.pt-BR.vtt 2.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.en.vtt 2.7 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.en.vtt 2.7 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.en.vtt 2.7 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/08. 06 Precision SC V1-q2wVorBfefU.pt-BR.vtt 2.7 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.en.vtt 2.7 kB
Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.en.vtt 2.7 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.en.vtt 2.7 kB
Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.en-US.vtt 2.7 kB
Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.zh-CN.vtt 2.7 kB
Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.en.vtt 2.7 kB
Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.en.vtt 2.7 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.pt-BR.vtt 2.7 kB
Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.pt-BR.vtt 2.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt 2.7 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt 2.7 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.pt-BR.vtt 2.7 kB
Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.en.vtt 2.7 kB
Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.pt-BR.vtt 2.7 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.pt-BR.vtt 2.7 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.pt-BR.vtt 2.7 kB
Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.zh-CN.vtt 2.7 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.pt-BR.vtt 2.7 kB
Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.en.vtt 2.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.en.vtt 2.7 kB
Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.en-US.vtt 2.7 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.en.vtt 2.7 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/08. 06 Precision SC V1-q2wVorBfefU.en.vtt 2.8 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.pt-BR.vtt 2.8 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.pt-BR.vtt 2.8 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.pt-BR.vtt 2.8 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.en.vtt 2.8 kB
Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.en.vtt 2.8 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.en.vtt 2.8 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.es-MX.vtt 2.8 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.en.vtt 2.8 kB
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.en.vtt 2.8 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.zh-CN.vtt 2.8 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.zh-CN.vtt 2.8 kB
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.ar.vtt 2.8 kB
Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.zh-CN.vtt 2.8 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.en.vtt 2.8 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.zh-CN.vtt 2.8 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.zh-CN.vtt 2.8 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.es-MX.vtt 2.8 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.es-MX.vtt 2.8 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.es-MX.vtt 2.8 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.es-MX.vtt 2.8 kB
Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.zh-CN.vtt 2.8 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.en.vtt 2.8 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.en.vtt 2.8 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.en.vtt 2.8 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.en.vtt 2.8 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.en-US.vtt 2.8 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.pt-BR.vtt 2.8 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.zh-CN.vtt 2.8 kB
Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.pt-BR.vtt 2.8 kB
Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.en.vtt 2.8 kB
Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.pt-BR.vtt 2.8 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.pt-BR.vtt 2.8 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.pt-BR.vtt 2.9 kB
Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.en.vtt 2.9 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.zh-CN.vtt 2.9 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/09. 07 Recall SC V1-0n5wUZiefkQ.pt-BR.vtt 2.9 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/05. When Accuracy Wont Work-r0-O-gIDXZ0.pt-BR.vtt 2.9 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.zh-CN.vtt 2.9 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hidden-errors.gif 2.9 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.zh-CN.vtt 2.9 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.en.vtt 2.9 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.zh-CN.vtt 2.9 kB
Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.pt-BR.vtt 2.9 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/05. When Accuracy Wont Work-r0-O-gIDXZ0.en.vtt 2.9 kB
Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.zh-CN.vtt 2.9 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.zh-CN.vtt 2.9 kB
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.pt-BR.vtt 2.9 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.pt-BR.vtt 2.9 kB
Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.ar.vtt 2.9 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.en.vtt 2.9 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.pt-BR.vtt 2.9 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.pt-BR.vtt 2.9 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Answer False Negatives And Positives-KOytJL1lvgg.en.vtt 2.9 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.pt-BR.vtt 2.9 kB
Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.pt-BR.vtt 2.9 kB
Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.zh-CN.vtt 2.9 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.pt-BR.vtt 2.9 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/weight-label-reference.gif 2.9 kB
Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.zh-CN.vtt 2.9 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Answer False Negatives And Positives-KOytJL1lvgg.pt-BR.vtt 2.9 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.en.vtt 2.9 kB
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.en.vtt 2.9 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.zh-CN.vtt 2.9 kB
Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.en.vtt 2.9 kB
Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.en-US.vtt 2.9 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.pt-BR.vtt 2.9 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.en.vtt 2.9 kB
Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.zh-CN.vtt 2.9 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.zh-CN.vtt 2.9 kB
Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.pt-BR.vtt 2.9 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.zh-CN.vtt 2.9 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.zh-CN.vtt 3.0 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.en.vtt 3.0 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.zh-CN.vtt 3.0 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.zh-CN.vtt 3.0 kB
Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.zh-CN.vtt 3.0 kB
Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.zh-CN.vtt 3.0 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.en.vtt 3.0 kB
Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.zh-CN.vtt 3.0 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.en.vtt 3.0 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.zh-CN.vtt 3.0 kB
Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.zh-CN.vtt 3.0 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.zh-CN.vtt 3.0 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.pt-BR.vtt 3.0 kB
Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.en.vtt 3.0 kB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.ar.vtt 3.0 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/backprop-error.gif 3.0 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.en.vtt 3.0 kB
Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.en.vtt 3.0 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.en-US.vtt 3.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.en.vtt 3.0 kB
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.pt-BR.vtt 3.0 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.en.vtt 3.0 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.en.vtt 3.0 kB
Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.en.vtt 3.0 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.pt-BR.vtt 3.0 kB
Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.pt-BR.vtt 3.0 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.pt-BR.vtt 3.0 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.en.vtt 3.0 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.pt-BR.vtt 3.0 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.en.vtt 3.0 kB
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.en.vtt 3.1 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.en.vtt 3.1 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.zh-CN.vtt 3.1 kB
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.ar.vtt 3.1 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.zh-CN.vtt 3.1 kB
Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.pt-BR.vtt 3.1 kB
Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.zh-CN.vtt 3.1 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.en.vtt 3.1 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.en-US.vtt 3.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.en.vtt 3.1 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.zh-CN.vtt 3.1 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.zh-CN.vtt 3.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.en.vtt 3.1 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.pt-BR.vtt 3.1 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.pt-BR.vtt 3.1 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.en.vtt 3.1 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.en-US.vtt 3.1 kB
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.ar.vtt 3.1 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.pt-BR.vtt 3.1 kB
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.pt-BR.vtt 3.1 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.pt-BR.vtt 3.1 kB
Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.zh-CN.vtt 3.1 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.zh-CN.vtt 3.1 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.zh-CN.vtt 3.1 kB
Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.zh-CN.vtt 3.1 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.pt-BR.vtt 3.1 kB
Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.en.vtt 3.1 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.en.vtt 3.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.zh-CN.vtt 3.1 kB
Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.zh-CN.vtt 3.1 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/09. 07 Recall SC V1-0n5wUZiefkQ.en.vtt 3.1 kB
Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.ar.vtt 3.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.en.vtt 3.1 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.pt-BR.vtt 3.1 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.en.vtt 3.1 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.zh-CN.vtt 3.1 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.en.vtt 3.1 kB
Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.es-MX.vtt 3.2 kB
Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.pt-BR.vtt 3.2 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.pt-BR.vtt 3.2 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.pt-BR.vtt 3.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.zh-CN.vtt 3.2 kB
Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.en.vtt 3.2 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.zh-CN.vtt 3.2 kB
Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.pt-BR.vtt 3.2 kB
Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.pt-BR.vtt 3.2 kB
Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.pt-BR.vtt 3.2 kB
Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.pt-BR.vtt 3.2 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.en.vtt 3.2 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.zh-CN.vtt 3.2 kB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.zh-CN.vtt 3.2 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.pt-BR.vtt 3.2 kB
Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.pt-BR.vtt 3.2 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt 3.2 kB
Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt 3.2 kB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.ar.vtt 3.2 kB
Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.pt-BR.vtt 3.2 kB
Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.pt-BR.vtt 3.2 kB
Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.en.vtt 3.2 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.pt-BR.vtt 3.2 kB
Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.en-US.vtt 3.2 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.en.vtt 3.2 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.en.vtt 3.2 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.en-US.vtt 3.2 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.en-US.vtt 3.2 kB
Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.en.vtt 3.3 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.en.vtt 3.3 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.en.vtt 3.3 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.pt-BR.vtt 3.3 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.pt-BR.vtt 3.3 kB
Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.en.vtt 3.3 kB
Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.en-US.vtt 3.3 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.es-MX.vtt 3.3 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/mse.png 3.3 kB
Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.en.vtt 3.3 kB
Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.en.vtt 3.3 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.es-MX.vtt 3.3 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.pt-BR.vtt 3.3 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.zh-CN.vtt 3.3 kB
Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.en.vtt 3.3 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.zh-CN.vtt 3.3 kB
Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.pt-BR.vtt 3.3 kB
Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.en.vtt 3.3 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.en.vtt 3.3 kB
Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.en-US.vtt 3.3 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.zh-CN.vtt 3.3 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.zh-CN.vtt 3.3 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.zh-CN.vtt 3.3 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.zh-CN.vtt 3.3 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.pt-BR.vtt 3.3 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt 3.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt 3.4 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.pt-BR.vtt 3.4 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.pt-BR.vtt 3.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.en.vtt 3.4 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.en.vtt 3.4 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.en-US.vtt 3.4 kB
Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.en.vtt 3.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.pt-BR.vtt 3.4 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.zh-CN.vtt 3.4 kB
Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.en-US.vtt 3.4 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.pt-BR.vtt 3.4 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/heaviside-step-function-2.gif 3.4 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.zh-CN.vtt 3.4 kB
Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.pt-BR.vtt 3.4 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.en.vtt 3.4 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.zh-CN.vtt 3.4 kB
Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.en-US.vtt 3.4 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.zh-CN.vtt 3.4 kB
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.ar.vtt 3.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.pt-BR.vtt 3.4 kB
Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.pt-BR.vtt 3.4 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.zh-CN.vtt 3.4 kB
Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.en.vtt 3.4 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.zh-CN.vtt 3.4 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.pt-BR.vtt 3.4 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt 3.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt 3.4 kB
Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.pt-BR.vtt 3.5 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.pt-BR.vtt 3.5 kB
Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.en.vtt 3.5 kB
Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.en-US.vtt 3.5 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1-NBWbluSbxPg.zh-CN.vtt 3.5 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.en.vtt 3.5 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.zh-CN.vtt 3.5 kB
Part 03-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution-G3fRVgLa5gI.pt-BR.vtt 3.5 kB
Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.pt-BR.vtt 3.5 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.en.vtt 3.5 kB
Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.zh-CN.vtt 3.5 kB
Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.pt-BR.vtt 3.5 kB
Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.en.vtt 3.5 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.en.vtt 3.5 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.en.vtt 3.5 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.en.vtt 3.5 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.zh-CN.vtt 3.5 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.en.vtt 3.5 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.zh-CN.vtt 3.5 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.pt-BR.vtt 3.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.zh-CN.vtt 3.5 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.zh-CN.vtt 3.5 kB
Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.pt-BR.vtt 3.5 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.pt-BR.vtt 3.5 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.en.vtt 3.5 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.en-US.vtt 3.5 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.pt-BR.vtt 3.5 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.en.vtt 3.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt 3.5 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt 3.5 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.pt-BR.vtt 3.5 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.en.vtt 3.5 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.en.vtt 3.5 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.en.vtt 3.5 kB
Part 11-Module 01-Lesson 01_Software and Tools/index.html 3.5 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.pt-BR.vtt 3.6 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.pt-BR.vtt 3.6 kB
Part 10-Module 01-Lesson 04_Land a Job Offer/index.html 3.6 kB
Part 04-Module 05-Lesson 01_PCA Mini-Project/index.html 3.6 kB
Part 05-Module 01-Lesson 06_Deep Learning Assessment/index.html 3.6 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.zh-CN.vtt 3.6 kB
Part 02-Module 04-Lesson 01_NumPy and pandas Assessment/index.html 3.6 kB
Part 04-Module 07-Lesson 01_Unsupervised Learning Assessment/index.html 3.6 kB
Part 06-Module 03-Lesson 01_Reinforcement Learning Assessment/index.html 3.6 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt 3.6 kB
Part 05-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt 3.6 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.pt-BR.vtt 3.6 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.en.vtt 3.6 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.en.vtt 3.6 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.pt-BR.vtt 3.6 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.en-US.vtt 3.6 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.en-US.vtt 3.6 kB
Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.en.vtt 3.6 kB
Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.zh-CN.vtt 3.6 kB
Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.en.vtt 3.6 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.en.vtt 3.6 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.zh-CN.vtt 3.6 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.en-US.vtt 3.6 kB
Part 03-Module 01-Lesson 07_Supervised Learning Assessment/index.html 3.6 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.zh-CN.vtt 3.6 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.en.vtt 3.6 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.en.vtt 3.6 kB
Part 03-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution-G3fRVgLa5gI.en.vtt 3.6 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.pt-BR.vtt 3.6 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.es-MX.vtt 3.6 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.zh-CN.vtt 3.6 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.pt-BR.vtt 3.7 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.zh-CN.vtt 3.7 kB
Part 02-Module 04-Lesson 02_Model Evaluation and Validation Assessment/index.html 3.7 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.en.vtt 3.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.zh-CN.vtt 3.7 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.zh-CN.vtt 3.7 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.en.vtt 3.7 kB
Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.zh-CN.vtt 3.7 kB
Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.zh-CN.vtt 3.7 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.pt-BR.vtt 3.7 kB
Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.zh-CN.vtt 3.7 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.en.vtt 3.7 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.en.vtt 3.7 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression-Metrics-906P4BPnl9A.zh-CN.vtt 3.7 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.en-US.vtt 3.7 kB
Part 10-Module 01-Lesson 01_Ace Your Interview/index.html 3.7 kB
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/index.html 3.7 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.pt-BR.vtt 3.7 kB
Part 04-Module 02-Lesson 02_Clustering Mini-Project/index.html 3.7 kB
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.zh-CN.vtt 3.7 kB
Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team-cuKecPpZ7PM.en.vtt 3.7 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.en.vtt 3.7 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.zh-CN.vtt 3.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt 3.8 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.es-MX.vtt 3.8 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.es-MX.vtt 3.8 kB
Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.ar.vtt 3.8 kB
Part 11-Module 01-Lesson 02_Deep Learning/index.html 3.8 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt 3.8 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.es-MX.vtt 3.8 kB
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.ar.vtt 3.8 kB
Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.zh-CN.vtt 3.8 kB
Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.zh-CN.vtt 3.8 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.pt-BR.vtt 3.8 kB
Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.zh-CN.vtt 3.8 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.pt-BR.vtt 3.8 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.pt-BR.vtt 3.8 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.pt-BR.vtt 3.8 kB
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.ar.vtt 3.8 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.zh-CN.vtt 3.8 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.zh-CN.vtt 3.8 kB
Part 01-Module 02-Lesson 01_Career Services Available to You/index.html 3.8 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.zh-CN.vtt 3.8 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/06. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.en.vtt 3.8 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/11. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.pt-BR.vtt 3.8 kB
Part 01-Module 01-Lesson 03_Introductory Practice Project/index.html 3.8 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.en.vtt 3.8 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.pt-BR.vtt 3.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.zh-CN.vtt 3.8 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/24. Neural Network Regression-aUJCBqBfEnI.pt-BR.vtt 3.8 kB
Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.zh-CN.vtt 3.8 kB
Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.pt-BR.vtt 3.9 kB
assets/css/styles.css 3.9 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.pt-BR.vtt 3.9 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.en.vtt 3.9 kB
assets/css/fonts/KaTeX_Size3-Regular.woff2 3.9 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.en.vtt 3.9 kB
Part 03-Module 01-Lesson 01_Linear Regression/07. Square Trick-AGZEq-yQgRM.pt-BR.vtt 3.9 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.zh-CN.vtt 3.9 kB
Part 10-Module 01-Lesson 03_Interview Fails/index.html 3.9 kB
Part 06-Module 01-Lesson 01_Introduction to RL/index.html 3.9 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.pt-BR.vtt 3.9 kB
Part 05-Module 01-Lesson 07_Deep Learning Project/index.html 3.9 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.en.vtt 3.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks-ISHGyvsT0QY.zh-CN.vtt 3.9 kB
Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.en.vtt 3.9 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.en.vtt 3.9 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/m.gif 3.9 kB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.pt-BR.vtt 3.9 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.pt-BR.vtt 3.9 kB
Part 09-Module 01-Lesson 02_LinkedIn Review/index.html 3.9 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax-4DxoYuR7aZ4.zh-CN.vtt 3.9 kB
Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team-cuKecPpZ7PM.pt-BR.vtt 3.9 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.pt-BR.vtt 3.9 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.zh-CN.vtt 3.9 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality-j231aRV74QM.zh-CN.vtt 3.9 kB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.en.vtt 3.9 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.pt-BR.vtt 3.9 kB
Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.pt-BR.vtt 3.9 kB
Part 05-Module 01-Lesson 01_Neural Networks/24. Gradient Descent-rhVIF-nigrY.en.vtt 3.9 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.en.vtt 3.9 kB
Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.en.vtt 3.9 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.zh-CN.vtt 4.0 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.pt-BR.vtt 4.0 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions-llakAjwox_8.zh-CN.vtt 4.0 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.en.vtt 4.0 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/index.html 4.0 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.en.vtt 4.0 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.pt-BR.vtt 4.0 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.zh-CN.vtt 4.0 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.en.vtt 4.0 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.en-US.vtt 4.0 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.pt-BR.vtt 4.0 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.pt-BR.vtt 4.0 kB
Part 03-Module 01-Lesson 03_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.pt-BR.vtt 4.0 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.en.vtt 4.0 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.en.vtt 4.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/07. Square Trick-AGZEq-yQgRM.en.vtt 4.0 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.en.vtt 4.0 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.zh-CN.vtt 4.0 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.pt-BR.vtt 4.0 kB
Part 05-Module 01-Lesson 02_Cloud Computing/index.html 4.0 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.pt-BR.vtt 4.0 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression-Metrics-906P4BPnl9A.pt-BR.vtt 4.0 kB
Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.ar.vtt 4.0 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.en.vtt 4.0 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.en.vtt 4.0 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions-KJLaRfOOPGA.zh-CN.vtt 4.0 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.en.vtt 4.0 kB
Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.ar.vtt 4.1 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/index.html 4.1 kB
Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.pt-BR.vtt 4.1 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples & Applications MAIN V1 V2-GhyFsjQ4FkA.zh-CN.vtt 4.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/24. Gradient Descent-rhVIF-nigrY.pt-BR.vtt 4.1 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.pt-BR.vtt 4.1 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1-NBWbluSbxPg.en.vtt 4.1 kB
Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.en.vtt 4.1 kB
Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.pt-BR.vtt 4.1 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.en.vtt 4.1 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.en-US.vtt 4.1 kB
Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/index.html 4.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.zh-CN.vtt 4.1 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/11. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.en.vtt 4.1 kB
Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.en.vtt 4.1 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.zh-CN.vtt 4.1 kB
Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.en-US.vtt 4.1 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/06. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.pt-BR.vtt 4.1 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.pt-BR.vtt 4.1 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/index.html 4.1 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.pt-BR.vtt 4.1 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.en.vtt 4.2 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.en.vtt 4.2 kB
Part 02-Module 03-Lesson 01_Model Selection/08. Grid Search SC V1-zDw-ZGiHW5I.en.vtt 4.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.zh-CN.vtt 4.2 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.en-US.vtt 4.2 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.en-US.vtt 4.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.pt-BR.vtt 4.2 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.zh-CN.vtt 4.2 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.pt-BR.vtt 4.2 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.pt-BR.vtt 4.2 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/index.html 4.2 kB
Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/index.html 4.2 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/index.html 4.2 kB
Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.en.vtt 4.2 kB
Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.en-US.vtt 4.2 kB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/index.html 4.2 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/index.html 4.2 kB
Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.en.vtt 4.2 kB
Part 04-Module 08-Lesson 01_Creating Customer Segments/index.html 4.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.zh-CN.vtt 4.2 kB
Part 05-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.zh-CN.vtt 4.2 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.en.vtt 4.2 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.en.vtt 4.2 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.en.vtt 4.2 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.zh-CN.vtt 4.2 kB
Part 05-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.en.vtt 4.2 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.en.vtt 4.2 kB
Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.en.vtt 4.2 kB
Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.en-US.vtt 4.2 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.pt-BR.vtt 4.2 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/index.html 4.2 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/index.html 4.2 kB
Part 05-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.zh-CN.vtt 4.2 kB
Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.en.vtt 4.2 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/index.html 4.2 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/index.html 4.3 kB
Part 02-Module 03-Lesson 01_Model Selection/08. Grid Search SC V1-zDw-ZGiHW5I.pt-BR.vtt 4.3 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.en.vtt 4.3 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.en.vtt 4.3 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.zh-CN.vtt 4.3 kB
Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.en.vtt 4.3 kB
Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.en-US.vtt 4.3 kB
Part 05-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.pt-BR.vtt 4.3 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.pt-BR.vtt 4.3 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.en.vtt 4.3 kB
Part 10-Module 02-Lesson 02_List-Based Collections/index.html 4.3 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/index.html 4.3 kB
Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy-piLpj1V1HEk.pt-BR.vtt 4.3 kB
Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-05-22-at-12.27.22-pm.png 4.3 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.pt-BR.vtt 4.3 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/maze.png 4.3 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.pt-BR.vtt 4.3 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.en.vtt 4.3 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/index.html 4.3 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.en-US.vtt 4.3 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/index.html 4.3 kB
Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.pt-BR.vtt 4.3 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression-Metrics-906P4BPnl9A.en-US.vtt 4.3 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/index.html 4.3 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.zh-CN.vtt 4.3 kB
Part 05-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.pt-BR.vtt 4.3 kB
Part 03-Module 01-Lesson 03_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.pt-BR.vtt 4.4 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.en.vtt 4.4 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.pt-BR.vtt 4.4 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/index.html 4.4 kB
Part 03-Module 01-Lesson 03_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.zh-CN.vtt 4.4 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/softmax-math.png 4.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.en.vtt 4.4 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.en.vtt 4.4 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.pt-BR.vtt 4.4 kB
Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-05-22-at-12.27.55-pm.png 4.4 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.pt-BR.vtt 4.4 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1-NBWbluSbxPg.pt-BR.vtt 4.4 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/index.html 4.4 kB
Part 10-Module 02-Lesson 06_Graphs/index.html 4.4 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/index.html 4.4 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.pt-BR.vtt 4.4 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/index.html 4.4 kB
Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy-piLpj1V1HEk.zh-CN.vtt 4.4 kB
Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.ar.vtt 4.5 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples & Applications MAIN V1 V2-GhyFsjQ4FkA.pt-BR.vtt 4.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.zh-CN.vtt 4.5 kB
Part 03-Module 01-Lesson 03_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.en.vtt 4.5 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.pt-BR.vtt 4.5 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks-ISHGyvsT0QY.en.vtt 4.5 kB
Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.ar.vtt 4.5 kB
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.en.vtt 4.5 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/index.html 4.5 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/index.html 4.5 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.zh-CN.vtt 4.5 kB
Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.pt-BR.vtt 4.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.en.vtt 4.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.en.vtt 4.5 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.zh-CN.vtt 4.5 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/index.html 4.5 kB
Part 03-Module 01-Lesson 03_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.zh-CN.vtt 4.5 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.en.vtt 4.5 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.en-US.vtt 4.5 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/index.html 4.5 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.en.vtt 4.5 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks-ISHGyvsT0QY.pt-BR.vtt 4.6 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.ar.vtt 4.6 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.pt-BR.vtt 4.6 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.zh-CN.vtt 4.6 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.pt-BR.vtt 4.6 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.pt-BR.vtt 4.6 kB
Part 02-Module 03-Lesson 01_Model Selection/index.html 4.6 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/index.html 4.6 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.en.vtt 4.6 kB
Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.pt-BR.vtt 4.6 kB
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.pt-BR.vtt 4.6 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples & Applications MAIN V1 V2-GhyFsjQ4FkA.en.vtt 4.6 kB
Part 03-Module 01-Lesson 01_Linear Regression/11. Minimizing Error Functions-RbT2TXN_6tY.en.vtt 4.6 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/index.html 4.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.zh-CN.vtt 4.6 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.es-MX.vtt 4.6 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.zh-CN.vtt 4.6 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/index.html 4.6 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.pt-BR.vtt 4.6 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/index.html 4.6 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer-dRFd6HaAXys.zh-CN.vtt 4.6 kB
Part 05-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.pt-BR.vtt 4.7 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.pt-BR.vtt 4.7 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.pt-BR.vtt 4.7 kB
Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-jjdbGD4CBGk.en.vtt 4.7 kB
Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.pt-BR.vtt 4.7 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.en.vtt 4.7 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax-4DxoYuR7aZ4.en.vtt 4.7 kB
Part 03-Module 01-Lesson 01_Linear Regression/11. Minimizing Error Functions-RbT2TXN_6tY.pt-BR.vtt 4.7 kB
Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.ar.vtt 4.7 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.pt-BR.vtt 4.7 kB
Part 03-Module 01-Lesson 04_Naive Bayes/index.html 4.7 kB
Part 04-Module 03-Lesson 01_Feature Scaling/index.html 4.7 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality-j231aRV74QM.en.vtt 4.7 kB
Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.ar.vtt 4.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.zh-CN.vtt 4.7 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/04. M2L3 04 V1-QicxmyE5vTo.zh-CN.vtt 4.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.pt-BR.vtt 4.7 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.zh-CN.vtt 4.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.en.vtt 4.8 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.en.vtt 4.8 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.zh-CN.vtt 4.8 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions-KJLaRfOOPGA.en.vtt 4.8 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.zh-CN.vtt 4.8 kB
Part 10-Module 01-Lesson 05_Interview Practice/index.html 4.8 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.pt-BR.vtt 4.8 kB
assets/css/fonts/KaTeX_Size3-Regular.woff 4.8 kB
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.zh-CN.vtt 4.8 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions-llakAjwox_8.en.vtt 4.8 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.zh-CN.vtt 4.8 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.zh-CN.vtt 4.8 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/index.html 4.8 kB
Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.zh-CN.vtt 4.8 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer-L5QBqYDNJn0.zh-CN.vtt 4.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.zh-CN.vtt 4.8 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.en.vtt 4.8 kB
Part 05-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.en.vtt 4.8 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.en.vtt 4.8 kB
Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-RyThtU8GcT0.zh-CN.vtt 4.8 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/index.html 4.8 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.en-US.vtt 4.8 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.pt-BR.vtt 4.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.zh-CN.vtt 4.9 kB
Part 10-Module 02-Lesson 05_Trees/index.html 4.9 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.pt-BR.vtt 4.9 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.pt-BR.vtt 4.9 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/index.html 4.9 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.zh-CN.vtt 4.9 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer-dRFd6HaAXys.en.vtt 4.9 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.zh-CN.vtt 4.9 kB
Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy-piLpj1V1HEk.en.vtt 4.9 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.en.vtt 4.9 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.zh-CN.vtt 4.9 kB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.ar.vtt 4.9 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/index.html 4.9 kB
Part 05-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.en.vtt 4.9 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer-L5QBqYDNJn0.pt-BR.vtt 4.9 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.en.vtt 4.9 kB
Part 04-Module 02-Lesson 01_Clustering/index.html 4.9 kB
Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.zh-CN.vtt 5.0 kB
Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.ar.vtt 5.0 kB
Part 05-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.en.vtt 5.0 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.en.vtt 5.0 kB
Part 03-Module 01-Lesson 03_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.en.vtt 5.0 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.en.vtt 5.0 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.zh-CN.vtt 5.0 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/index.html 5.0 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/index.html 5.0 kB
Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-B3w_msqHP68.zh-CN.vtt 5.1 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.zh-CN.vtt 5.1 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.zh-CN.vtt 5.1 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.zh-CN.vtt 5.1 kB
Part 03-Module 01-Lesson 03_Decision Trees/index.html 5.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.zh-CN.vtt 5.1 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.zh-CN.vtt 5.1 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.zh-CN.vtt 5.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.pt-BR.vtt 5.1 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.en.vtt 5.1 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.en.vtt 5.1 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.en-US.vtt 5.1 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.en-US.vtt 5.1 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.en.vtt 5.1 kB
Part 05-Module 01-Lesson 07_Deep Learning Project/01. Dog Breed Recognition Project.html 5.1 kB
Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer.html 5.2 kB
Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting.html 5.2 kB
Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviews are Conversations.html 5.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.zh-CN.vtt 5.2 kB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.pt-BR.vtt 5.2 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality-j231aRV74QM.pt-BR.vtt 5.2 kB
assets/css/fonts/KaTeX_Size4-Regular.woff2 5.2 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.en.vtt 5.2 kB
Part 04-Module 05-Lesson 01_PCA Mini-Project/01. PCA Mini-Project.html 5.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.pt-BR.vtt 5.2 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.pt-BR.vtt 5.3 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.ar.vtt 5.3 kB
Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails.html 5.3 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.ar.vtt 5.3 kB
Part 11-Module 01-Lesson 02_Deep Learning/02. What You'll Watch and Learn.html 5.3 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer-L5QBqYDNJn0.en.vtt 5.3 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.zh-CN.vtt 5.3 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.pt-BR.vtt 5.3 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.zh-CN.vtt 5.3 kB
Part 05-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.pt-BR.vtt 5.3 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.es-MX.vtt 5.3 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.zh-CN.vtt 5.3 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.pt-BR.vtt 5.3 kB
Part 09-Module 02-Lesson 01_GitHub Review/index.html 5.3 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction.html 5.3 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/index.html 5.3 kB
Part 03-Module 01-Lesson 01_Linear Regression/08. Gradient Descent-4s4x9h6AN5Y.pt-BR.vtt 5.4 kB
Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.zh-CN.vtt 5.4 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary.html 5.4 kB
Part 03-Module 01-Lesson 01_Linear Regression/index.html 5.4 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/03. M2L3 03 V2-TePX-0Bs23E.zh-CN.vtt 5.4 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions-llakAjwox_8.pt-BR.vtt 5.4 kB
Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales.html 5.4 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.zh-CN.vtt 5.4 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.pt-BR.vtt 5.4 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/08. Recap.html 5.4 kB
Part 04-Module 02-Lesson 02_Clustering Mini-Project/03. Solution.html 5.4 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset.html 5.4 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.en.vtt 5.4 kB
Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara.html 5.4 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/05. Policy Gradients.html 5.4 kB
Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.en.vtt 5.4 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/01. Policy-Based Methods.html 5.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.en.vtt 5.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.pt-BR.vtt 5.4 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/04. Stochastic Policy Search.html 5.4 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/02. Why Policy-Based Methods.html 5.4 kB
Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit.html 5.4 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/index.html 5.4 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer-dRFd6HaAXys.pt-BR.vtt 5.4 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/06. Monte Carlo Policy Gradients.html 5.4 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/07. Constrained Policy Gradients.html 5.4 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/03. Policy Function Approximation.html 5.4 kB
Part 04-Module 02-Lesson 02_Clustering Mini-Project/02. K-means clustering of movie ratings.html 5.4 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions-KJLaRfOOPGA.pt-BR.vtt 5.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.en.vtt 5.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.zh-CN.vtt 5.5 kB
Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.ar.vtt 5.5 kB
Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.pt-BR.vtt 5.5 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.zh-CN.vtt 5.5 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/04. M2L3 04 V1-QicxmyE5vTo.en.vtt 5.5 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.zh-CN.vtt 5.5 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.pt-BR.vtt 5.5 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.en.vtt 5.5 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity.html 5.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.en.vtt 5.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.en.vtt 5.5 kB
Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-B3w_msqHP68.en.vtt 5.5 kB
Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.en.vtt 5.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.zh-CN.vtt 5.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.en.vtt 5.5 kB
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/03. Mini Project.html 5.5 kB
Part 05-Module 01-Lesson 02_Cloud Computing/07. More Resources.html 5.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.en.vtt 5.5 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.zh-CN.vtt 5.5 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. A Better Score Function.html 5.5 kB
Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.zh-CN.vtt 5.5 kB
Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.zh-CN.vtt 5.5 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/09. Week 2 Plan.html 5.5 kB
Part 01-Module 01-Lesson 03_Introductory Practice Project/04. Titanic Survival Exploration.html 5.5 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. The Actor and The Critic.html 5.5 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.pt-BR.vtt 5.5 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer.html 5.5 kB
Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction.html 5.6 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.en.vtt 5.6 kB
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.ar.vtt 5.6 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. Advantage Function.html 5.6 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/08. Week 1 Plan.html 5.6 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. Two Function Approximators.html 5.6 kB
assets/css/fonts/KaTeX_Size2-Regular.woff2 5.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.en.vtt 5.6 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. Actor-Critic Methods.html 5.6 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.pt-BR.vtt 5.6 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/02. Outline.html 5.6 kB
Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.zh-CN.vtt 5.6 kB
Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.pt-BR.vtt 5.6 kB
Part 05-Module 01-Lesson 07_Deep Learning Project/02. Dog Breed Workspace.html 5.6 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/11. Outro.html 5.6 kB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. Actor-Critic with Advantage.html 5.6 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.pt-BR.vtt 5.6 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/01. Intro.html 5.6 kB
Part 06-Module 01-Lesson 01_Introduction to RL/06. Reference Guide.html 5.6 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/02. Bagging.html 5.6 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.en.vtt 5.6 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/03. AdaBoost.html 5.6 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.en-US.vtt 5.6 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.en-US.vtt 5.6 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.pt-BR.vtt 5.7 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/01. Intro.html 5.7 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.en.vtt 5.7 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing.html 5.7 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/10. Outro.html 5.7 kB
Part 11-Module 01-Lesson 02_Deep Learning/01. Deep Learning.html 5.7 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/index.html 5.7 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions.html 5.7 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem.html 5.7 kB
Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.zh-CN.vtt 5.7 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/index.html 5.7 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Intro.html 5.7 kB
Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.zh-CN.vtt 5.7 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm.html 5.7 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/04. Weighting the Data.html 5.7 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps.html 5.7 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming.html 5.7 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/08. Combining the Models.html 5.7 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/07. ICA in sklearn.html 5.7 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem.html 5.7 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch.html 5.7 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Problems 2.html 5.7 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.zh-CN.vtt 5.7 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/07. Weighting the Models 3.html 5.7 kB
Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.zh-CN.vtt 5.7 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction.html 5.7 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.en.vtt 5.7 kB
Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-RyThtU8GcT0.en.vtt 5.7 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.pt-BR.vtt 5.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.pt-BR.vtt 5.7 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm.html 5.7 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/inputs-matrix.png 5.7 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Why Use Elevator Pitches.html 5.7 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.pt-BR.vtt 5.7 kB
Part 03-Module 01-Lesson 01_Linear Regression/08. Gradient Descent-4s4x9h6AN5Y.en.vtt 5.7 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.zh-CN.vtt 5.8 kB
Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.pt-BR.vtt 5.8 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.zh-CN.vtt 5.8 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem.html 5.8 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps.html 5.8 kB
Part 11-Module 01-Lesson 02_Deep Learning/03. Deep Learning What You'll Do.html 5.8 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/08. Tuning Parameters Automatically.html 5.8 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing.html 5.8 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/03. Random Projection in sklearn.html 5.8 kB
Part 05-Module 01-Lesson 01_Neural Networks/index.html 5.8 kB
Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists.html 5.8 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/index.html 5.8 kB
Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.ar.vtt 5.8 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax.html 5.8 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/index.html 5.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.pt-BR.vtt 5.8 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.en.vtt 5.8 kB
Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays.html 5.8 kB
Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks.html 5.8 kB
Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues.html 5.8 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter.html 5.8 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.en-US.vtt 5.8 kB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms.html 5.8 kB
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.pt-BR.vtt 5.8 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/03. Stats Refresher.html 5.8 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.zh-CN.vtt 5.8 kB
assets/css/fonts/KaTeX_Size1-Regular.woff2 5.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.zh-CN.vtt 5.8 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency.html 5.8 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps.html 5.8 kB
Part 01-Module 01-Lesson 03_Introductory Practice Project/01. Overview.html 5.8 kB
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.en.vtt 5.8 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/05. When accuracy won't work.html 5.8 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.en.vtt 5.8 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.en.vtt 5.8 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2-CUTtQvxKkNw.zh-CN.vtt 5.8 kB
Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists.html 5.8 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.zh-CN.vtt 5.9 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/03. Course Expectations.html 5.9 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.en.vtt 5.9 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve.html 5.9 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.zh-CN.vtt 5.9 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/10. Resources.html 5.9 kB
Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details.html 5.9 kB
Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.pt-BR.vtt 5.9 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued.html 5.9 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction.html 5.9 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/diagonal-line-1.png 5.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/diagonal-line-1.png 5.9 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/perceptron-formula.gif 5.9 kB
Part 02-Module 03-Lesson 01_Model Selection/04. K-Fold Cross Validation.html 5.9 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.pt-BR.vtt 5.9 kB
Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth.html 5.9 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression Metrics.html 5.9 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose of the Cover Letter.html 5.9 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/index.html 5.9 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.zh-CN.vtt 5.9 kB
Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections.html 5.9 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.pt-BR.vtt 5.9 kB
Part 02-Module 03-Lesson 01_Model Selection/12. Summary.html 5.9 kB
Part 10-Module 02-Lesson 06_Graphs/10. DFS.html 5.9 kB
Part 10-Module 02-Lesson 06_Graphs/11. BFS.html 5.9 kB
Part 04-Module 02-Lesson 02_Clustering Mini-Project/01. Intro.html 5.9 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies.html 5.9 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.pt-BR.vtt 5.9 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.en-US.vtt 5.9 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.zh-CN.vtt 5.9 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.pt-BR.vtt 6.0 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality.html 6.0 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/01. Introduction.html 6.0 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Intro to Deep Neural Networks.html 6.0 kB
Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources.html 6.0 kB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/05. Project Workspace.html 6.0 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up.html 6.0 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.pt-BR.vtt 6.0 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.pt-BR.vtt 6.0 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.en.vtt 6.0 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/05. FastICA Algorithm.html 6.0 kB
Part 04-Module 08-Lesson 01_Creating Customer Segments/06. Workspace.html 6.0 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies.html 6.0 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris.html 6.0 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example.html 6.0 kB
Part 10-Module 02-Lesson 06_Graphs/04. Connectivity.html 6.0 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/08. Experience.html 6.0 kB
Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path.html 6.0 kB
Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.en.vtt 6.0 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.en.vtt 6.0 kB
Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal.html 6.0 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure.html 6.0 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/06. Project Workspace.html 6.0 kB
Part 05-Module 01-Lesson 02_Cloud Computing/02. Create an AWS Account.html 6.0 kB
Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph.html 6.0 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys.html 6.0 kB
Part 02-Module 03-Lesson 01_Model Selection/13. Outro.html 6.0 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection.html 6.0 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure.html 6.0 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection.html 6.1 kB
Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices.html 6.1 kB
Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction.html 6.1 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout.html 6.1 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.pt-BR.vtt 6.1 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization.html 6.1 kB
Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles.html 6.1 kB
Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations.html 6.1 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion Matrix 2.html 6.1 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2.html 6.1 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.pt-BR.vtt 6.1 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning.html 6.1 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary.html 6.1 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/08. Coding 2.html 6.1 kB
Part 02-Module 03-Lesson 01_Model Selection/01. Types of Errors.html 6.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.en.vtt 6.1 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.zh-CN.vtt 6.1 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.en-US.vtt 6.1 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/index.html 6.1 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components.html 6.1 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning.html 6.1 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding.html 6.1 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.zh-CN.vtt 6.1 kB
Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.zh-CN.vtt 6.1 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely.html 6.1 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure.html 6.1 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/03. M2L3 03 V2-TePX-0Bs23E.en.vtt 6.1 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2.html 6.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.pt-BR.vtt 6.1 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components.html 6.1 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.pt-BR.vtt 6.1 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions.html 6.1 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding.html 6.1 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network.html 6.1 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.pt-BR.vtt 6.1 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.en.vtt 6.1 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection.html 6.1 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization.html 6.1 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely.html 6.1 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions.html 6.2 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.zh-CN.vtt 6.2 kB
Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.zh-CN.vtt 6.2 kB
Part 11-Module 01-Lesson 01_Software and Tools/01. TensorFlow.html 6.2 kB
Part 01-Module 02-Lesson 01_Career Services Available to You/02. Access Your Career Portal.html 6.2 kB
Part 02-Module 03-Lesson 01_Model Selection/03. Cross Validation.html 6.2 kB
Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.zh-CN.vtt 6.2 kB
Part 10-Module 01-Lesson 05_Interview Practice/10. Arpan's Analysis of the Interview.html 6.2 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network.html 6.2 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/02. Quadcopter workspace.html 6.2 kB
Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.zh-CN.vtt 6.2 kB
Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction.html 6.2 kB
Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.pt-BR.vtt 6.2 kB
Part 10-Module 01-Lesson 05_Interview Practice/01. Introduction.html 6.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.en.vtt 6.2 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Precision and Recall.html 6.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.pt-BR.vtt 6.2 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components.html 6.2 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/perceptron-equation-2.gif 6.2 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.pt-BR.vtt 6.2 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely.html 6.2 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction.html 6.2 kB
Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-05-22-at-12.25.34-pm.png 6.2 kB
Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.en.vtt 6.2 kB
Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.es-MX.vtt 6.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.zh-CN.vtt 6.2 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. Welcome to the Machine Learning Engineer Nanodegree Program.html 6.2 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation.html 6.2 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion.html 6.3 kB
Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves.html 6.3 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces.html 6.3 kB
Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team.html 6.3 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.zh-CN.vtt 6.3 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.en.vtt 6.3 kB
Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.en-US.vtt 6.3 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.en.vtt 6.3 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/08. [Lab] Independent Component Analysis.html 6.3 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort.html 6.3 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort.html 6.3 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.pt-BR.vtt 6.3 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort.html 6.3 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/09. [Solution] Independent Component Analysis.html 6.3 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation.html 6.3 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets.html 6.3 kB
Part 03-Module 01-Lesson 04_Naive Bayes/01. Intro.html 6.3 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.en.vtt 6.3 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search.html 6.3 kB
Part 03-Module 01-Lesson 04_Naive Bayes/16. Outro.html 6.3 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning.html 6.3 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro.html 6.3 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.en.vtt 6.3 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting.html 6.3 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.en.vtt 6.3 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning.html 6.3 kB
Part 03-Module 01-Lesson 04_Naive Bayes/05. Bayes Theorem.html 6.3 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network.html 6.3 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro.html 6.4 kB
Part 01-Module 01-Lesson 03_Introductory Practice Project/02. Software Requirements.html 6.4 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.pt-BR.vtt 6.4 kB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/01. Project Overview.html 6.4 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/06. Skills.html 6.4 kB
Part 03-Module 01-Lesson 04_Naive Bayes/02. Guess the Person.html 6.4 kB
Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn.html 6.4 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort.html 6.4 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort.html 6.4 kB
Part 03-Module 01-Lesson 04_Naive Bayes/03. Known and Inferred.html 6.4 kB
Part 02-Module 03-Lesson 01_Model Selection/08. Grid Search.html 6.4 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences.html 6.4 kB
Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.pt-BR.vtt 6.4 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay.html 6.4 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0).html 6.4 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/18. Outro.html 6.4 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0).html 6.4 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company.html 6.4 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort.html 6.4 kB
Part 03-Module 01-Lesson 04_Naive Bayes/07. Solution False Positives.html 6.4 kB
Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.pt-BR.vtt 6.4 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/Project Description - Teach a Quadcopter How to Fly.html 6.4 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences.html 6.4 kB
Part 01-Module 01-Lesson 03_Introductory Practice Project/03. Project files.html 6.4 kB
Part 03-Module 01-Lesson 04_Naive Bayes/09. Bayesian Learning 2.html 6.4 kB
Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.en.vtt 6.4 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search.html 6.4 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/01. Overview.html 6.4 kB
Part 04-Module 04-Lesson 01_PCA/index.html 6.4 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure.html 6.4 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace.html 6.4 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding.html 6.4 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation.html 6.4 kB
Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.en.vtt 6.4 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.zh-CN.vtt 6.4 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.zh-CN.vtt 6.4 kB
Part 09-Module 01-Lesson 02_LinkedIn Review/02. Resources in Your Career Portal.html 6.4 kB
Part 03-Module 01-Lesson 04_Naive Bayes/12. Naive Bayes Algorithm 2.html 6.4 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging.html 6.4 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries.html 6.4 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.en.vtt 6.4 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases.html 6.4 kB
Part 10-Module 02-Lesson 05_Trees/13. BST Complications.html 6.4 kB
assets/css/fonts/KaTeX_Size4-Regular.woff 6.5 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative.html 6.5 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/08. Exercise Tile Coding.html 6.5 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/03. Analyzing Behavioral Answers.html 6.5 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming.html 6.5 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.ar.vtt 6.5 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!.html 6.5 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences.html 6.5 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/05. Resources in Your Career Portal.html 6.5 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.pt-BR.vtt 6.5 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/06. Exercise Discretization.html 6.5 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/04. Independent Component Analysis (ICA).html 6.5 kB
Part 09-Module 01-Lesson 02_LinkedIn Review/Project Description - LinkedIn Profile Review Project.html 6.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/16. RBF Kernel 3.html 6.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/14. RBF Kernel 1.html 6.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/15. RBF Kernel 2.html 6.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/07. Margin Error.html 6.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/01. Intro.html 6.5 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis.html 6.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/09. Error Function.html 6.5 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.pt-BR.vtt 6.5 kB
Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales.html 6.5 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/12. TensorFlow Implementation.html 6.5 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs.html 6.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/10. The C Parameter.html 6.5 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks.html 6.5 kB
Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/05. Submitting the Project.html 6.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/04. Error Function Intuition.html 6.5 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis.html 6.5 kB
Part 02-Module 03-Lesson 01_Model Selection/10. Grid Search Lab.html 6.5 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/07. Program Readiness.html 6.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/11. Polynomial Kernel 1.html 6.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/13. Polynomial Kernel 3.html 6.5 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/index.html 6.5 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.pt-BR.vtt 6.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/03. Minimizing Distances.html 6.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/06. Classification Error.html 6.5 kB
Part 10-Module 01-Lesson 01_Ace Your Interview/03. STAR Method.html 6.5 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction.html 6.5 kB
Part 10-Module 01-Lesson 05_Interview Practice/03. Analyzing an Interview.html 6.5 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.zh-CN.vtt 6.5 kB
Part 02-Module 03-Lesson 01_Model Selection/11. [Solution] Grid Search Lab.html 6.5 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question.html 6.5 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/01. Introduction.html 6.6 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review.html 6.6 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.zh-CN.vtt 6.6 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/z93yz2vrgdaacqjowbaabie8yaaackcwmaaadshdeaaabpwhgaaia0yqwaaecamayaacbngamaajamjaeaaegtxgaaakqjywaaankemqaaagncgaaagdrhdaaaqjowbgaaie0yawaakcamaqaasbpgaaaapaljaaaa0oqxaaaaaciyaacangemaabamjagaaagtrgdaacqjowbaabie8yaaackcwmaaadshdeaaabpwhgaaia0yqwaaeca.png 6.6 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review.html 6.6 kB
Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.pt-BR.vtt 6.6 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/02. Two-Layer Neural Network.html 6.6 kB
Part 10-Module 02-Lesson 05_Trees/12. BSTs.html 6.6 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.pt-BR.vtt 6.6 kB
Part 10-Module 02-Lesson 05_Trees/15. Heaps.html 6.6 kB
Part 10-Module 02-Lesson 05_Trees/01. Trees.html 6.6 kB
Part 04-Module 02-Lesson 01_Clustering/13. Sklearn.html 6.6 kB
Part 10-Module 02-Lesson 05_Trees/09. Insert.html 6.6 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction.html 6.6 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/02. Getting Started.html 6.6 kB
Part 10-Module 02-Lesson 05_Trees/16. Heapify.html 6.6 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.en.vtt 6.6 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression Answer.html 6.6 kB
Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.en.vtt 6.6 kB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/04. Uploading to Workspace.html 6.6 kB
Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.pt-BR.vtt 6.6 kB
Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.en.vtt 6.6 kB
Part 05-Module 01-Lesson 02_Cloud Computing/01. Overview.html 6.6 kB
Part 10-Module 02-Lesson 05_Trees/02. Tree Basics.html 6.6 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm.html 6.6 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning.html 6.6 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review.html 6.7 kB
Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal.html 6.7 kB
Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations.html 6.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.pt-BR.vtt 6.7 kB
Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie.html 6.7 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2.html 6.7 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.en.vtt 6.7 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3.html 6.7 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/Project Description - Resume Review Project (Career Change).html 6.7 kB
Part 04-Module 08-Lesson 01_Creating Customer Segments/05. Uploading to Workspace.html 6.7 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.pt-BR.vtt 6.7 kB
Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies.html 6.7 kB
Part 03-Module 01-Lesson 04_Naive Bayes/04. Guess the Person Now.html 6.7 kB
Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology.html 6.7 kB
Part 10-Module 02-Lesson 05_Trees/08. Search and Delete.html 6.7 kB
assets/css/fonts/KaTeX_Size2-Regular.woff 6.7 kB
Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.pt-BR.vtt 6.7 kB
Part 03-Module 01-Lesson 04_Naive Bayes/10. Bayesian Learning 3.html 6.7 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/05. Uploading to Workspace.html 6.7 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/03. Replay Buffer.html 6.7 kB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/02. Starting the project.html 6.7 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward.html 6.7 kB
Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills.html 6.7 kB
Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation.html 6.7 kB
Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees.html 6.7 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa.html 6.7 kB
Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning.html 6.7 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.en.vtt 6.7 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax.html 6.7 kB
Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees.html 6.7 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.en.vtt 6.7 kB
Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy.html 6.7 kB
Part 03-Module 01-Lesson 03_Decision Trees/20. Outro.html 6.7 kB
Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals.html 6.7 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up.html 6.7 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis.html 6.7 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/08. Resources in Your Career Portal.html 6.7 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited.html 6.7 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. K-means considerations.html 6.7 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/17. Further Reading.html 6.7 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.en.vtt 6.7 kB
Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means.html 6.7 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format.html 6.7 kB
Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.en.vtt 6.7 kB
Part 03-Module 01-Lesson 04_Naive Bayes/15. Spam Classifier - Workspace.html 6.7 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1.html 6.7 kB
Part 04-Module 08-Lesson 01_Creating Customer Segments/03. Starting the project.html 6.8 kB
Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-ooqFCXMdxys.zh-CN.vtt 6.8 kB
Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.pt-BR.vtt 6.8 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values.html 6.8 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. Overview of other clustering methods.html 6.8 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. Hierarchical clustering single-link.html 6.8 kB
Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion.html 6.8 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/Project Description - Resume Review Project (Prior Industry Experience).html 6.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/diagonal-line-2.png 6.8 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/diagonal-line-2.png 6.8 kB
Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/04. Proposal Guidelines.html 6.8 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks.html 6.8 kB
Part 03-Module 01-Lesson 03_Decision Trees/01. Intro.html 6.8 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/03. Starting the project.html 6.8 kB
Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps 3.html 6.8 kB
Part 05-Module 01-Lesson 02_Cloud Computing/04. Apply Credits.html 6.8 kB
Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.en.vtt 6.8 kB
Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3.html 6.8 kB
Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/02. Description.html 6.8 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch.html 6.8 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It.html 6.8 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/Project Description - Resume Review Project (Entry-level).html 6.8 kB
Part 05-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.zh-CN.vtt 6.8 kB
Part 03-Module 01-Lesson 04_Naive Bayes/14. Project.html 6.8 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. Perceptrons.html 6.8 kB
Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain.html 6.9 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions.html 6.9 kB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/03. Submitting the project.html 6.9 kB
Part 03-Module 01-Lesson 03_Decision Trees/15. Random Forests.html 6.9 kB
Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/04. Report Guidelines.html 6.9 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/10. ICA Applications.html 6.9 kB
Part 04-Module 08-Lesson 01_Creating Customer Segments/04. Submitting the project.html 6.9 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. DBSCAN implementation.html 6.9 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/10. F1 Score.html 6.9 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.en.vtt 6.9 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent The Math.html 6.9 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/04. Submitting the project.html 6.9 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.zh-CN.vtt 6.9 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/02. Course Outline.html 6.9 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/Project Description - Udacity Professional Profile Review.html 6.9 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing-gmxGRJSKEb0.zh-CN.vtt 6.9 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. Higher Dimensions.html 6.9 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction.html 6.9 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classification Problems 1.html 6.9 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.pt-BR.vtt 6.9 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/15. Mini Project TD (Part 4).html 6.9 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/12. Mini Project TD (Part 3).html 6.9 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/09. Mini Project TD (Part 2).html 6.9 kB
Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/01. Overview.html 6.9 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.pt-BR.vtt 6.9 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. Complete-link, average-link, Ward.html 6.9 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/05. Mini Project TD (Parts 0 and 1).html 6.9 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. Examining single-link clustering.html 7.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/23. Outro.html 7.0 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/15. Quick Sort Practice.html 7.0 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.zh-CN.vtt 7.0 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2-CUTtQvxKkNw.en.vtt 7.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/07. Square Trick.html 7.0 kB
assets/css/fonts/KaTeX_Size1-Regular.woff 7.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/05. Moving a Line.html 7.0 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements.html 7.0 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/05. Recruitment Data.html 7.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick.html 7.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/22. Regularization.html 7.0 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/03. Personal Branding.html 7.0 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. Hierarchical clustering implementation.html 7.0 kB
Part 04-Module 08-Lesson 01_Creating Customer Segments/02. Software Requirements.html 7.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/08. Gradient Descent.html 7.0 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.en.vtt 7.0 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/01. Random Projection.html 7.0 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.en-US.vtt 7.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions.html 7.0 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/09. AdaBoost in sklearn.html 7.0 kB
Part 01-Module 02-Lesson 01_Career Services Available to You/03. Your Udacity Professional Profile.html 7.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error.html 7.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/04. Fitting a Line Through Data.html 7.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error.html 7.0 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy.html 7.0 kB
Part 10-Module 02-Lesson 05_Trees/07. Tree Traversal Practice.html 7.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/01. Intro.html 7.0 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/13. [Lab] DBSCAN.html 7.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/21. Polynomial Regression.html 7.0 kB
Part 03-Module 01-Lesson 03_Decision Trees/06. Solution Student Admissions.html 7.0 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/23. Summary.html 7.0 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/14. [Lab Solution] DBSCAN.html 7.1 kB
Part 03-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices.html 7.1 kB
Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.en-US.vtt 7.1 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/08. Resources in Your Career Portal.html 7.1 kB
Part 04-Module 08-Lesson 01_Creating Customer Segments/01. Overview.html 7.1 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.zh-CN.vtt 7.1 kB
Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes #1.html 7.1 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/07. [Lab] Hierarchical clustering .html 7.1 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/05. Perceptron Algorithm.html 7.1 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.en.vtt 7.1 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/10. Quiz Action-Value Functions.html 7.1 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/02. Software Requirements.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/13. Support Vector Machines.html 7.1 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/08. [Lab Solution] Hierarchical Clustering.html 7.1 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/09. Resources in Your Career Portal.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/01. What Is Machine Learning.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/15. Support Vector Machines Answer.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method.html 7.1 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/11. Implementing Deep Q-Learning.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer.html 7.1 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.en.vtt 7.1 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.pt-BR.vtt 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer.html 7.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.zh-CN.vtt 7.1 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/08. Resources in Your Career Portal.html 7.1 kB
Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering.html 7.1 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer.html 7.1 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/08. Resources in Your Career Portal.html 7.1 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/05. Weighting the Models 1.html 7.1 kB
Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository.html 7.2 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.en.vtt 7.2 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. DBSCAN.html 7.2 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer.html 7.2 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization Quiz.html 7.2 kB
Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3.html 7.2 kB
Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2.html 7.2 kB
Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1.html 7.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.zh-CN.vtt 7.2 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion.html 7.2 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/06. ICA.html 7.2 kB
Part 03-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution.html 7.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.pt-BR.vtt 7.2 kB
Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/01. Overview.html 7.2 kB
Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.ar.vtt 7.2 kB
Part 09-Module 02-Lesson 01_GitHub Review/16. Outro.html 7.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values.html 7.2 kB
Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.en.vtt 7.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values.html 7.2 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/08. Resources in Your Career Portal.html 7.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean.html 7.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha, Part 1.html 7.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration.html 7.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation.html 7.2 kB
Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula 3.html 7.2 kB
Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2.html 7.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement.html 7.2 kB
Part 03-Module 01-Lesson 03_Decision Trees/19. [Solution] Titanic Survival Model.html 7.2 kB
Part 03-Module 01-Lesson 03_Decision Trees/18. Titanic Survival Model with Decision Trees.html 7.3 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.pt-BR.vtt 7.3 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/11. Implementation.html 7.3 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.pt-BR.vtt 7.3 kB
Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph.html 7.3 kB
Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2.html 7.3 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/02. Random Projection.html 7.3 kB
Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications.html 7.3 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.en.vtt 7.3 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions.html 7.3 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.pt-BR.vtt 7.3 kB
Part 05-Module 01-Lesson 01_Neural Networks/29. Outro.html 7.3 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2.html 7.3 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.zh-CN.vtt 7.3 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.zh-CN.vtt 7.4 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. DBSCAN examples & applications.html 7.4 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction.html 7.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.en.vtt 7.4 kB
Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.en.vtt 7.4 kB
Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.en.vtt 7.4 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.en.vtt 7.4 kB
Part 03-Module 01-Lesson 04_Naive Bayes/13. Building a Spam Classifier.html 7.4 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2-CUTtQvxKkNw.pt-BR.vtt 7.4 kB
Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter.html 7.4 kB
Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.pt-BR.vtt 7.4 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.pt-BR.vtt 7.4 kB
Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction.html 7.4 kB
Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items.html 7.5 kB
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris.html 7.5 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. ROC Curve-2Iw5TiGzJI4.zh-CN.vtt 7.5 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve-2Iw5TiGzJI4.zh-CN.vtt 7.5 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/04. Quiz Space Representations.html 7.5 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/07. Ornstein–Uhlenbeck Noise.html 7.5 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. HC examples and applications.html 7.5 kB
Part 03-Module 01-Lesson 03_Decision Trees/13. Solution Information Gain.html 7.5 kB
Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.pt-BR.vtt 7.5 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0)-CsD6b0csU7o.zh-CN.vtt 7.5 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/08. Precision.html 7.5 kB
Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/06. Submitting the Project.html 7.5 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.en.vtt 7.5 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation.html 7.5 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.en.vtt 7.5 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.pt-BR.vtt 7.5 kB
Part 10-Module 01-Lesson 05_Interview Practice/12. Resources in Your Career Portal.html 7.5 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return.html 7.5 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing-gmxGRJSKEb0.pt-BR.vtt 7.5 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/24. Neural Network Regression.html 7.5 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration.html 7.5 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration.html 7.5 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/14. Support Vector Machines Quiz.html 7.5 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement.html 7.5 kB
Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects.html 7.5 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression Quiz.html 7.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/02. Which line is better.html 7.6 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method, Part 1.html 7.6 kB
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron.html 7.6 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/10. 08 F1 Score SC V1-TRzBeL07fSg.pt-BR.vtt 7.6 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/03. Decision Trees Quiz.html 7.6 kB
Part 03-Module 01-Lesson 04_Naive Bayes/06. Quiz False Positives.html 7.6 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components.html 7.6 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/13. Resources in Your Career Portal.html 7.6 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. GMM in 2D.html 7.6 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/20. Quiz Silhouette Coefficient .html 7.6 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration.html 7.6 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.pt-BR.vtt 7.6 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation.html 7.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/08. Mini Project MC (Part 2).html 7.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/17. Mini Project MC (Part 3).html 7.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/21. Mini Project MC (Part 4).html 7.6 kB
Part 04-Module 02-Lesson 01_Clustering/01. Introduction.html 7.6 kB
Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.ar.vtt 7.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/05. Mini Project MC (Parts 0 and 1).html 7.6 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. Intro.html 7.6 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let's Get Started .html 7.6 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. Cluster Validation.html 7.6 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.en.vtt 7.6 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What is Deep Learning .html 7.6 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout.html 7.6 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. GMM Implementation.html 7.6 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum.html 7.6 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Outro.html 7.6 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/Project Description - Finding Donors for CharityML.html 7.6 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/12. Quiz Expectation Maximization.html 7.7 kB
Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/03. Software and Data Requirements.html 7.7 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/09. Recall.html 7.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. Parameter Hyperspace .html 7.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification.html 7.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. Measuring Performance .html 7.7 kB
Part 02-Module 04-Lesson 01_NumPy and pandas Assessment/01. Assessment.html 7.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Mini Project Intro.html 7.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction.html 7.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/15. Local Minima.html 7.7 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. GMM Clustering in One Dimension.html 7.7 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. Cluster Analysis Process.html 7.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. Minimizing Cross Entropy.html 7.7 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/18. Kernel Method Quiz.html 7.7 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.en.vtt 7.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart.html 7.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization 2.html 7.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions.html 7.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate Decay.html 7.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-linear Data.html 7.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding.html 7.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/21. Stochastic Gradient Descent.html 7.7 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/05. Gaussian Distribution in 2D.html 7.7 kB
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight.html 7.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models.html 7.7 kB
Part 02-Module 03-Lesson 01_Model Selection/09. Grid Search in sklearn.html 7.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions.html 7.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big and Small .html 7.7 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. Gaussian Distribution in One Dimension.html 7.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient.html 7.7 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/11. Visual Example of EM Progress.html 7.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier .html 7.7 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/10. Expectation Maximization Part 2.html 7.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Early Stopping.html 7.7 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/12. Polynomial Kernel 2.html 7.7 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/06. Naive Bayes Quiz.html 7.7 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/09. Expectation Maximization Part 1.html 7.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. Momentum and Learning Rate Decay.html 7.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks.html 7.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization.html 7.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. Optimizing a Logistic Classifier.html 7.7 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons.html 7.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Problems 2.html 7.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs and Initial Weights .html 7.7 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.pt-BR.vtt 7.7 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. Gaussian Mixture Model (GMM) Clustering.html 7.7 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.pt-BR.vtt 7.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding.html 7.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity.html 7.8 kB
Part 10-Module 02-Lesson 05_Trees/04. Tree Practice.html 7.8 kB
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1.html 7.8 kB
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2.html 7.8 kB
Part 05-Module 01-Lesson 01_Neural Networks/25. Logistic Regression Algorithm.html 7.8 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/08. Coding 2-qEteyPNRSwU.en.vtt 7.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers (Part 1).html 7.8 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. Practical Aspects of Learning.html 7.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.zh-CN.vtt 7.8 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/07. Resume Review (Entry-level).html 7.8 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Quiz.html 7.8 kB
Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/05. Example Reports.html 7.8 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World.html 7.8 kB
Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages.html 7.8 kB
Part 03-Module 01-Lesson 04_Naive Bayes/11. Naive Bayes Algorithm 1.html 7.8 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing-gmxGRJSKEb0.en-US.vtt 7.8 kB
Part 05-Module 01-Lesson 01_Neural Networks/01. Announcement.html 7.8 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.pt-BR.vtt 7.8 kB
Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-bXpONCq5ePE.zh-CN.vtt 7.8 kB
Part 10-Module 02-Lesson 02_List-Based Collections/12. Queue Practice.html 7.8 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent.html 7.8 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/21. GMM & Cluster Validation Lab.html 7.8 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/07. Resume Review (Career Change).html 7.9 kB
Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/03. Software and Data Requirements.html 7.9 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/22. GMM & Cluster Validation Lab Solution.html 7.9 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. Overview of The Expectation Maximization (EM) Algorithm.html 7.9 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/12. Merge Sort Practice.html 7.9 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/14. Implementation.html 7.9 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. External Validation Indices.html 7.9 kB
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters.html 7.9 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/09. Bubble Sort Practice.html 7.9 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The data.html 7.9 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion.html 7.9 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Intro.html 7.9 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/07. Resume Review (Prior Industry Experience).html 7.9 kB
Part 05-Module 01-Lesson 01_Neural Networks/20. Cross-Entropy 1.html 7.9 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis.html 8.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization.html 8.0 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/11. Time for Live Practice with Pramp.html 8.0 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/22. Mini Project DP (Part 5).html 8.0 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/16. Mini Project DP (Part 3).html 8.0 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/19. Mini Project DP (Part 4).html 8.0 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/25. Mini Project DP (Part 6).html 8.0 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/13. Mini Project DP (Part 2).html 8.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix.html 8.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. Comparing our Results with Doctors.html 8.0 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.en.vtt 8.0 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/10. Mini Project DP (Parts 0 and 1).html 8.0 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.pt-BR.vtt 8.0 kB
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric.html 8.0 kB
Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/02. Description.html 8.0 kB
Part 05-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.pt-BR.vtt 8.0 kB
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition).html 8.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve.html 8.0 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. Internal Validation Indices.html 8.0 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/16. Implementation.html 8.0 kB
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3.html 8.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Probability of Skin Cancer.html 8.0 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Quiz.html 8.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification.html 8.0 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/04. Top Section.html 8.0 kB
Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2.html 8.0 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers.html 8.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating the Training.html 8.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges.html 8.0 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/06. Community Guidelines.html 8.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction.html 8.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training the Neural Network.html 8.1 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1.html 8.1 kB
Part 03-Module 01-Lesson 03_Decision Trees/03. Recommending Apps 2.html 8.1 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/Project Description - Craft Your Cover Letter.html 8.1 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/07. M2L3 07 V2-ZBLLGIN1EfU.zh-CN.vtt 8.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions.html 8.1 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example.html 8.1 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.pt-BR.vtt 8.1 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/20. Implementation.html 8.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/28. Perceptron vs Gradient Descent.html 8.1 kB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/Project Description - Predicting Boston Housing Prices.html 8.1 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/18. Quiz Adjusted Rand Index.html 8.1 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well .html 8.1 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.en.vtt 8.1 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What is the network looking at.html 8.1 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivity and Specificity.html 8.1 kB
Part 03-Module 01-Lesson 03_Decision Trees/09. Entropy Formula 2.html 8.1 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/10. 08 F1 Score SC V1-TRzBeL07fSg.en.vtt 8.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/28. Lab IMDB Data in Keras.html 8.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/27. Notebook Gradient Descent.html 8.2 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction.html 8.2 kB
Part 05-Module 01-Lesson 01_Neural Networks/img/codecogseqn-43.gif 8.2 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.pt-BR.vtt 8.2 kB
Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.ar.vtt 8.2 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random vs Pre-initialized Weight.html 8.2 kB
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means.html 8.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/09. Lab Student Admissions in Keras.html 8.2 kB
Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves SC V1-ZNhnNVKl8NM.en.vtt 8.2 kB
Part 09-Module 02-Lesson 01_GitHub Review/17. Resources in Your Career Portal.html 8.2 kB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters.html 8.2 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/07. Projects.html 8.2 kB
Part 03-Module 01-Lesson 03_Decision Trees/05. Quiz Student Admissions.html 8.2 kB
Part 05-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.en.vtt 8.2 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/Project Description - Technical Interview Practice.html 8.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/25. Neural Networks Playground.html 8.2 kB
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2.html 8.2 kB
Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-ooqFCXMdxys.en.vtt 8.2 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/06. False Negatives and Positives.html 8.2 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/03. Binary Search Practice.html 8.2 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.en.vtt 8.3 kB
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/01. Introduction.html 8.3 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build.html 8.3 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/04. Implementation.html 8.3 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/16. [Quiz] DBSCAN.html 8.3 kB
Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves SC V1-ZNhnNVKl8NM.pt-BR.vtt 8.3 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.en.vtt 8.3 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/02. M2L3 02 V2-ToS8vXGdODE.zh-CN.vtt 8.3 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve-2Iw5TiGzJI4.pt-BR.vtt 8.3 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. ROC Curve-2Iw5TiGzJI4.pt-BR.vtt 8.3 kB
Part 04-Module 08-Lesson 01_Creating Customer Segments/Project Description - Creating Customer Segments.html 8.3 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/Project Rubric - Teach a Quadcopter How to Fly.html 8.3 kB
Part 03-Module 01-Lesson 01_Linear Regression/02. Quiz Housing Prices.html 8.3 kB
Part 03-Module 01-Lesson 01_Linear Regression/12. Mean vs Total Error.html 8.4 kB
assets/css/fonts/KaTeX_Size3-Regular.ttf 8.4 kB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/10. [Quiz] Hierarchical clustering.html 8.4 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Quiz Numerical Stability.html 8.4 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.pt-BR.vtt 8.4 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.en.vtt 8.4 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/02. Resources.html 8.4 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.pt-BR.vtt 8.4 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.zh-CN.vtt 8.4 kB
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization.html 8.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries.html 8.5 kB
Part 05-Module 01-Lesson 07_Deep Learning Project/Project Description - Dog Breed Classifier.html 8.5 kB
Part 10-Module 01-Lesson 05_Interview Practice/11. Keep Practicing!.html 8.5 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/25. Solution Pooling Practice.html 8.5 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/18. Implementation.html 8.5 kB
Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA.html 8.5 kB
Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code.html 8.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs for Image Classification.html 8.5 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/07. Load Factor.html 8.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras.html 8.5 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/27. Solution Average Pooling.html 8.5 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/02. OpenAI Gym CliffWalkingEnv.html 8.5 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.zh-CN.vtt 8.5 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/22. Optimizers in Keras.html 8.5 kB
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2.html 8.5 kB
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands).html 8.5 kB
Part 10-Module 02-Lesson 06_Graphs/05. Graph Practice.html 8.5 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. Image Challenges.html 8.5 kB
Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA.html 8.5 kB
Part 04-Module 04-Lesson 01_PCA/26. Applying PCA to Real Data.html 8.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.zh-CN.vtt 8.5 kB
Part 10-Module 02-Lesson 02_List-Based Collections/04. Python Lists.html 8.5 kB
Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation.html 8.6 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module.html 8.6 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions.html 8.6 kB
Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula 1.html 8.6 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers (Part 2).html 8.6 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix.html 8.6 kB
Part 09-Module 02-Lesson 01_GitHub Review/Project Description - Optimize Your GitHub Profile.html 8.6 kB
Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation.html 8.6 kB
Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project.html 8.6 kB
Part 06-Module 03-Lesson 01_Reinforcement Learning Assessment/01. Assessment.html 8.6 kB
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories.html 8.6 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/13. Solution Number of Parameters.html 8.6 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance.html 8.6 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks.html 8.6 kB
Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components.html 8.6 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore The Design Space.html 8.6 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. GMM Examples & Applications.html 8.7 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/19. Mini project CNNs in Keras.html 8.7 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/02. Self-Practice Behavioral Questions.html 8.7 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.pt-BR.vtt 8.7 kB
Part 03-Module 01-Lesson 01_Linear Regression/13. Mini-batch Gradient Descent.html 8.7 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Write the Introduction.html 8.7 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.en.vtt 8.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. One-Hot Encoding.html 8.7 kB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling.html 8.7 kB
Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain.html 8.7 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/16. Analyzing Performance.html 8.8 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/15. Solution Parameter Sharing.html 8.8 kB
Part 03-Module 01-Lesson 03_Decision Trees/11. Multiclass Entropy.html 8.8 kB
Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish.html 8.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/21. Mini project Image Augmentation in Keras.html 8.8 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro To CNNs.html 8.8 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/12. Regularization.html 8.8 kB
Part 05-Module 01-Lesson 02_Cloud Computing/03. Get Access to GPU Instances.html 8.8 kB
Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism.html 8.8 kB
Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.ar.vtt 8.8 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/08. Implementation.html 8.8 kB
Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work.html 8.8 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/06. Weighting the Models 2.html 8.8 kB
Part 02-Module 04-Lesson 02_Model Evaluation and Validation Assessment/01. Model Evaluation and Validation assessment.html 8.8 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/23. Solution Pooling Mechanics.html 8.9 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve-2Iw5TiGzJI4.en.vtt 8.9 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. ROC Curve-2Iw5TiGzJI4.en.vtt 8.9 kB
Part 05-Module 01-Lesson 01_Neural Networks/07. Perceptrons.html 8.9 kB
Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality.html 8.9 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/Project Rubric - Technical Interview Practice.html 8.9 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/06. Transition to Classification.html 8.9 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.zh-CN.vtt 8.9 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.zh-CN.vtt 8.9 kB
Part 05-Module 01-Lesson 01_Neural Networks/22. Multi-Class Cross Entropy.html 8.9 kB
Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn.html 8.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions continued.html 8.9 kB
Part 05-Module 01-Lesson 01_Neural Networks/06. Higher Dimensions.html 8.9 kB
Part 05-Module 01-Lesson 01_Neural Networks/03. Classification Problems 1.html 8.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/21. Solution Pooling Intuition.html 9.0 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/03. Customizing Your Profile.html 9.0 kB
Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/Project Rubric - Capstone Proposal.html 9.0 kB
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz.html 9.0 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/10. String Keys Practice.html 9.0 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/07. Implementation.html 9.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/20. Linear Regression Warnings.html 9.0 kB
Part 03-Module 01-Lesson 03_Decision Trees/12. Quiz Information Gain.html 9.0 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.pt-BR.vtt 9.0 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/08. Troubleshooting.html 9.0 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure.html 9.0 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/05. Feedforward.html 9.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. Quiz Diagnosing Cancer.html 9.0 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures.html 9.0 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/12. Next Steps.html 9.0 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/26. Quiz Average Pooling.html 9.0 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0)-CsD6b0csU7o.en.vtt 9.0 kB
Part 02-Module 03-Lesson 01_Model Selection/07. Solution Detecting Overfitting and Underfitting.html 9.1 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/35. CNNs - Additional Resources.html 9.1 kB
Part 03-Module 01-Lesson 01_Linear Regression/11. Minimizing Error Functions.html 9.1 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/launch.png 9.1 kB
Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.ar.vtt 9.1 kB
Part 03-Module 01-Lesson 07_Supervised Learning Assessment/01. Supervised Learning Assessment.html 9.1 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.pt-BR.vtt 9.1 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. Quiz Random vs Pre-initialized Weights.html 9.1 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.pt-BR.vtt 9.2 kB
Part 05-Module 01-Lesson 01_Neural Networks/img/codecogseqn-60-2.png 9.2 kB
Part 09-Module 02-Lesson 01_GitHub Review/Project Rubric - Optimize Your GitHub Profile.html 9.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.en.vtt 9.2 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/03. Python Dictionaries.html 9.2 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/27. Useful Resources.html 9.2 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.en.vtt 9.2 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/24. Quiz Pooling Practice.html 9.2 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. Quiz ROC Curve.html 9.2 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Write the Body.html 9.2 kB
Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-bXpONCq5ePE.en.vtt 9.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images.html 9.2 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color.html 9.2 kB
Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/Project Description - Capstone Proposal.html 9.2 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.zh-CN.vtt 9.3 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. Quiz Data Challenges.html 9.3 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/05. Quiz Episodic or Continuing.html 9.3 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks-Mqogpnp1lrU.zh-CN.vtt 9.3 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/06. Python The Basics.html 9.3 kB
Part 04-Module 07-Lesson 01_Unsupervised Learning Assessment/01. Assessment.html 9.3 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.pt-BR.vtt 9.3 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/02. M2L3 02 V2-ToS8vXGdODE.en.vtt 9.3 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/05. Recursion Practice.html 9.3 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras.html 9.3 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy.html 9.3 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/12. Implementation.html 9.3 kB
Part 06-Module 02-Lesson 03_Policy-Based Methods/07. M2L3 07 V2-ZBLLGIN1EfU.en.vtt 9.3 kB
Part 10-Module 02-Lesson 05_Trees/14. BST Practice.html 9.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.pt-BR.vtt 9.4 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/05. DDPG Critic.html 9.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/14. Log-loss Error Function.html 9.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/23. Logistic Regression.html 9.4 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/20. Quiz Pooling Intuition.html 9.4 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/34. Solution TensorFlow Pooling Layer.html 9.5 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.pt-BR.vtt 9.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/26. Pre-Lab Gradient Descent.html 9.5 kB
Part 09-Module 01-Lesson 02_LinkedIn Review/01. Using LinkedIn.html 9.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.pt-BR.vtt 9.5 kB
Part 09-Module 02-Lesson 01_GitHub Review/10. Commit messages best practices.html 9.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/02. OpenAI Gym BlackjackEnv.html 9.6 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Trick.html 9.6 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/11. Efficiency Practice.html 9.6 kB
Part 05-Module 01-Lesson 01_Neural Networks/19. Maximizing Probabilities.html 9.6 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example.html 9.6 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/14. Project Description.html 9.6 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras.html 9.6 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/03. Quiz TensorFlow ReLUs.html 9.6 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/12. Quiz Number of Parameters.html 9.7 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/10. Quiz Convolution Output Shape.html 9.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.en.vtt 9.7 kB
Part 03-Module 01-Lesson 04_Naive Bayes/08. Bayesian Learning 1.html 9.7 kB
Part 05-Module 01-Lesson 02_Cloud Computing/06. Login to the Instance.html 9.7 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/04. Implementation.html 9.7 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent-Math-7sxA5Ap8AWM.zh-CN.vtt 9.7 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.en.vtt 9.7 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. Quiz Sensitivity and Specificity.html 9.7 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.en-US.vtt 9.7 kB
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again).html 9.7 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.en.vtt 9.7 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.en.vtt 9.7 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.en-US.vtt 9.7 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/11. Quiz Incremental Mean.html 9.8 kB
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters.html 9.8 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/14. Quiz Parameter Sharing.html 9.8 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.pt-BR.vtt 9.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/22. Quiz Pooling Mechanics.html 9.9 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/05. Udacity Support.html 9.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/32. Solution TensorFlow Convolution Layer.html 9.9 kB
Part 10-Module 02-Lesson 05_Trees/11. Binary Tree Practice.html 9.9 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks-xFu1_2K2D2U.zh-CN.vtt 9.9 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/04. Loading data into Pandas.html 9.9 kB
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance.html 9.9 kB
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality.html 9.9 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.zh-CN.vtt 10.0 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/01. Project Intro.html 10.0 kB
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes.html 10.0 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks-xFu1_2K2D2U.pt-BR.vtt 10.0 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks-Mqogpnp1lrU.pt-BR.vtt 10.0 kB
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two.html 10.0 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/04. DDPG Actor.html 10.0 kB
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous.html 10.0 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/15. Implementation.html 10.0 kB
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System.html 10.0 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs (Part 1).html 10.0 kB
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data.html 10.0 kB
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz.html 10.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. Skin Cancer.html 10.1 kB
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality.html 10.1 kB
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality.html 10.1 kB
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers.html 10.1 kB
Part 03-Module 01-Lesson 03_Decision Trees/02. Recommending Apps 1.html 10.1 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/21. Implementation.html 10.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood.html 10.1 kB
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile.html 10.1 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/04. Installing TensorFlow.html 10.1 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/Project Rubric - Craft Your Cover Letter.html 10.1 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/03. Your Workspace.html 10.2 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/11. Solution Convolution Output Shape.html 10.2 kB
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss.html 10.2 kB
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System.html 10.2 kB
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information.html 10.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.en.vtt 10.2 kB
Part 10-Module 01-Lesson 05_Interview Practice/Project Rubric - ML Interview Practice.html 10.2 kB
Part 05-Module 01-Lesson 06_Deep Learning Assessment/01. Assessment.html 10.3 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/05. Hello, Tensor World!.html 10.3 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/Project Rubric - Udacity Professional Profile Review.html 10.3 kB
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance.html 10.3 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/07. Finetuning.html 10.3 kB
Part 05-Module 01-Lesson 01_Neural Networks/21. Cross-Entropy 2.html 10.3 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/24. Implementation.html 10.4 kB
Part 04-Module 04-Lesson 01_PCA/17. Composite Features.html 10.4 kB
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz.html 10.4 kB
assets/css/fonts/KaTeX_Caligraphic-Regular.woff2 10.4 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks-xFu1_2K2D2U.en.vtt 10.4 kB
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data.html 10.4 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks-Mqogpnp1lrU.en.vtt 10.4 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/06. AND Perceptron Quiz.html 10.5 kB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition.html 10.5 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.en.vtt 10.5 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/10. Quiz TensorFlow Softmax.html 10.5 kB
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature.html 10.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.en.vtt 10.5 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/33. TensorFlow Pooling Layer.html 10.6 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. Solution Diagnosing Cancer.html 10.6 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/17. TensorFlow Convolution Layer.html 10.6 kB
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component of New System.html 10.6 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/11. F-beta Score.html 10.6 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/17. Summary.html 10.6 kB
assets/css/fonts/KaTeX_Caligraphic-Bold.woff2 10.6 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/05. Python Practice.html 10.6 kB
Part 03-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.pt-BR.vtt 10.6 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/07. Quiz Gaussian Mixtures.html 10.6 kB
Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/Project Description - Capstone Project.html 10.7 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/02. OpenAI Gym FrozenLakeEnv.html 10.7 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations.html 10.7 kB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/Project Rubric - Predicting Boston Housing Prices.html 10.7 kB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two.html 10.7 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/img/smalldf.png 10.8 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing your models.html 10.8 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/06. DDPG Agent.html 10.8 kB
Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Error vs Squared Error.html 10.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs for Image Classification.html 10.9 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/26. Check Your Understanding.html 10.9 kB
Part 10-Module 02-Lesson 02_List-Based Collections/10. Stack Practice.html 10.9 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/13. Quiz TensorFlow Cross Entropy.html 10.9 kB
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate.html 10.9 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/15. Categorical Cross-Entropy.html 10.9 kB
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA.html 10.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/19. TensorFlow Max Pooling.html 11.0 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.zh-CN.vtt 11.0 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/16. Max Pooling Layers in Keras.html 11.0 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/05. NumPy Arrays.html 11.0 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/08. XOR Perceptron Quiz.html 11.0 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent-Math-7sxA5Ap8AWM.en.vtt 11.1 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/07. OR & NOT Perceptron Quiz.html 11.1 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent-Math-7sxA5Ap8AWM.pt-BR.vtt 11.1 kB
Part 10-Module 01-Lesson 05_Interview Practice/Project Description - ML Interview Practice.html 11.1 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/15. More on Sensitivity and Specificity.html 11.1 kB
Part 03-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.en.vtt 11.1 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature Map Sizes.html 11.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/08. Mini project Training an MLP on MNIST.html 11.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.pt-BR.vtt 11.3 kB
assets/css/fonts/KaTeX_Size4-Regular.ttf 11.3 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/13. Summary.html 11.3 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. Refresh on ROC Curves.html 11.3 kB
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/02. Instructions.html 11.3 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/15. Quiz One-Step Dynamics, Part 1.html 11.4 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/Project Rubric - Finding Donors for CharityML.html 11.4 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/10. Quiz Testing in sklearn.html 11.5 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/neilsen-pic.png 11.5 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/08. (Optional) Margin Error Calculation.html 11.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Trick.html 11.6 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/18. Finite MDPs.html 11.6 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.pt-BR.vtt 11.6 kB
Part 04-Module 08-Lesson 01_Creating Customer Segments/Project Rubric - Creating Customer Segments.html 11.6 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/img/career-portal-sidebar.png 11.6 kB
Part 09-Module 02-Lesson 01_GitHub Review/img/career-portal-sidebar.png 11.6 kB
Part 10-Module 01-Lesson 05_Interview Practice/img/career-portal-sidebar.png 11.6 kB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/img/career-portal-sidebar.png 11.6 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/career-portal-sidebar.png 11.6 kB
Part 10-Module 02-Lesson 08_Technical Interview - Python/img/career-portal-sidebar.png 11.6 kB
Part 08-Module 01-Lesson 01_Conduct a Job Search/img/career-portal-sidebar.png 11.6 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/img/career-portal-sidebar.png 11.6 kB
Part 09-Module 01-Lesson 02_LinkedIn Review/img/career-portal-sidebar.png 11.6 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/career-portal-sidebar.png 11.6 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/img/career-portal-sidebar.png 11.6 kB
Part 01-Module 02-Lesson 01_Career Services Available to You/img/screen-shot-2017-10-27-at-1.49.58-pm.png 11.6 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.en.vtt 11.6 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/index.jpg 11.8 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/07. Quiz An Iterative Method.html 11.8 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/12. Gradient Descent The Code.html 11.9 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/04. Deep Neural Network in TensorFlow.html 11.9 kB
assets/css/fonts/KaTeX_Caligraphic-Regular.woff 11.9 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation.html 11.9 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.zh-CN.vtt 11.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/31. TensorFlow Convolution Layer.html 12.0 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/27. Pre-Lab IMDB Data in Keras.html 12.0 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.pt-BR.vtt 12.0 kB
Part 05-Module 01-Lesson 01_Neural Networks/16. Softmax.html 12.1 kB
assets/css/fonts/KaTeX_Caligraphic-Bold.woff 12.1 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/07. Tuning Parameters Manually.html 12.1 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/13. Convolutional Layers in Keras.html 12.2 kB
assets/css/fonts/KaTeX_Script-Regular.woff2 12.3 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/12. Quiz Pole-Balancing.html 12.3 kB
Part 10-Module 02-Lesson 02_List-Based Collections/07. Linked List Practice.html 12.3 kB
assets/css/fonts/KaTeX_Size2-Regular.ttf 12.4 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/19. Summary.html 12.4 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.en.vtt 12.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Neural Network Architecture.html 12.4 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/11. Action Values.html 12.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.pt-BR.vtt 12.5 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/03. Quiz Interpret the Policy.html 12.6 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.51.51-pm.png 12.6 kB
Part 03-Module 01-Lesson 03_Decision Trees/16. Hyperparameters.html 12.7 kB
Part 09-Module 01-Lesson 02_LinkedIn Review/Project Rubric - LinkedIn Profile Review Project.html 12.8 kB
Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/Project Rubric - Capstone Project.html 12.8 kB
Part 05-Module 01-Lesson 02_Cloud Computing/05. Launch an Instance.html 12.9 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/07. Quiz State-Value Functions.html 12.9 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/04. Quiz Test Your Intuition.html 13.0 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-10.05.37-pm.png 13.1 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/09. Implementation.html 13.1 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/edit-security-group.png 13.1 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/12. Quiz Optimal Policies.html 13.1 kB
assets/css/fonts/KaTeX_Size1-Regular.ttf 13.2 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/screen-shot-2017-10-02-at-10.41.44-am.png 13.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs.html 13.2 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/media/emevdpbVGr8UnjhurcR5buAbInIx5v4yYabDiWwX0DQNG3CyNOfFDn5hCCheyki9YPKZwIqQjkrf5ezPdcw=s0#w=210&h=192 13.3 kB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/media/unnamed-59153-0.gif 13.3 kB
Part 10-Module 01-Lesson 05_Interview Practice/img/quizimage.png 13.3 kB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/Project Rubric - Resume Review Project (Prior Industry Experience).html 13.3 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/09. Quiz Goals and Rewards.html 13.3 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Algorithm.html 13.3 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/backprop-network.png 13.4 kB
Part 03-Module 01-Lesson 01_Linear Regression/17. Multiple Linear Regression.html 13.5 kB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/Project Rubric - Resume Review Project (Entry-level).html 13.5 kB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/Project Rubric - Resume Review Project (Career Change).html 13.5 kB
Part 10-Module 02-Lesson 06_Graphs/08. Graph Representation Practice.html 13.6 kB
Part 05-Module 01-Lesson 07_Deep Learning Project/Project Rubric - Dog Breed Classifier.html 13.6 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/06. An Iterative Method, Part 2.html 13.6 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/09. The Simplest Neural Network.html 13.7 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent.html 13.8 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/aws-create-account.png 13.8 kB
assets/css/fonts/KaTeX_Script-Regular.woff 13.9 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.en.vtt 13.9 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.en-US.vtt 13.9 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/08. Pre-Lab Student Admissions in Keras.html 14.0 kB
assets/css/fonts/KaTeX_SansSerif-Regular.woff2 14.0 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/13. Quiz TensorFlow Dropout.html 14.0 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/09. Parameters.html 14.2 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/05. Intuition.html 14.2 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/27. Summary.html 14.3 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.pt-BR.vtt 14.5 kB
Part 03-Module 01-Lesson 01_Linear Regression/19. (Optional) Closed form Solution Math.html 14.7 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/img/dataframe.png 14.7 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/24. Visualizing CNNs (Part 2).html 14.8 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/25. Epochs.html 14.8 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/06. Filters.html 14.8 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/14. Quiz Epsilon-Greedy Policies.html 15.0 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/06. Save and Restore TensorFlow Models.html 15.0 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/16. Quiz One-Step Dynamics, Part 2.html 15.1 kB
assets/css/fonts/KaTeX_SansSerif-Italic.woff2 15.2 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/17. SVMs in sklearn.html 15.3 kB
Part 05-Module 01-Lesson 01_Neural Networks/11. Perceptron Algorithm.html 15.3 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/22. Summary.html 15.5 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Refresh on Confusion Matrices.html 15.7 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/19. MC Control Constant-alpha, Part 2.html 15.8 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/06. Training models in sklearn.html 15.9 kB
Part 05-Module 01-Lesson 01_Neural Networks/24. Gradient Descent.html 15.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/16. Visualizing CNNs.html 15.9 kB
assets/css/fonts/KaTeX_SansSerif-Bold.woff2 16.0 kB
Part 03-Module 01-Lesson 03_Decision Trees/17. Decision Trees in sklearn.html 16.0 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/review-and-launch.png 16.1 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/14. Quiz Dimensionality.html 16.7 kB
assets/css/fonts/KaTeX_SansSerif-Regular.woff 16.8 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/11. ReLU and Softmax Activation Functions.html 16.9 kB
Part 03-Module 01-Lesson 01_Linear Regression/15. Linear Regression in scikit-learn.html 17.0 kB
assets/css/fonts/KaTeX_Typewriter-Regular.woff2 17.5 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/img/two-layer-network.png 17.6 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. Perceptrons as Logical Operators.html 17.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/15. Exploration vs. Exploitation.html 17.9 kB
assets/css/fonts/KaTeX_SansSerif-Italic.woff 18.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/07. Keras.html 18.1 kB
Part 02-Module 03-Lesson 01_Model Selection/06. Detecting Overfitting and Underfitting with Learning Curves.html 18.7 kB
assets/css/fonts/KaTeX_Caligraphic-Regular.ttf 19.0 kB
assets/css/fonts/KaTeX_SansSerif-Bold.woff 19.2 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/30. Convolutional Network in TensorFlow.html 19.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning.html 19.3 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/04. Deadline Policy.html 19.4 kB
Part 10-Module 02-Lesson 06_Graphs/12. Graph Traversal Practice.html 19.5 kB
assets/css/fonts/KaTeX_Caligraphic-Bold.ttf 19.6 kB
Part 09-Module 01-Lesson 02_LinkedIn Review/media/SGdIHFzKav0QZmOSrrP69xch_F0Ufhu9pLy-nDXYDArHUyzAen7ewoLakVOKn3KvX_CVgJjBWkl_FmPTPqM=s0#w=250&h=120 19.6 kB
Part 09-Module 01-Lesson 02_LinkedIn Review/media/unnamed-project-desc-1.gif 19.6 kB
Part 05-Module 01-Lesson 01_Neural Networks/08. Perceptrons as Logical Operators.html 19.6 kB
assets/css/fonts/KaTeX_Fraktur-Regular.woff2 19.9 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/29. Mini Project Dermatologist AI.html 20.0 kB
assets/css/fonts/KaTeX_Math-BoldItalic.woff2 20.0 kB
assets/css/fonts/KaTeX_Math-Italic.woff2 20.4 kB
assets/css/fonts/KaTeX_Fraktur-Bold.woff2 20.5 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation.html 20.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/mnist-012.png 20.7 kB
assets/css/fonts/KaTeX_Typewriter-Regular.woff 20.9 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/img/student-acceptance.png 21.0 kB
assets/css/katex.min.css 22.1 kB
assets/css/fonts/KaTeX_Main-BoldItalic.woff2 22.2 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/05. Perceptron.html 22.2 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer Perceptrons.html 22.4 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-10.05.46-pm.png 22.5 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/16. Implementing Backpropagation.html 22.8 kB
assets/css/fonts/KaTeX_Fraktur-Regular.woff 22.8 kB
assets/css/fonts/KaTeX_Main-Italic.woff2 23.1 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/launch-instance.png 23.1 kB
assets/css/fonts/KaTeX_Math-BoldItalic.woff 23.2 kB
assets/css/fonts/KaTeX_Fraktur-Bold.woff 23.4 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/09. Quiz TensorFlow Linear Function.html 23.5 kB
assets/css/fonts/KaTeX_Math-Italic.woff 23.8 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/quadraticlinearregression.png 24.1 kB
assets/css/plyr.css 24.2 kB
assets/css/fonts/KaTeX_Script-Regular.ttf 24.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.51.47-pm.png 24.9 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/weights-0-1-2.png 25.2 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/max-pooling.png 25.8 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-25-at-11.35.38-am.png 25.8 kB
assets/css/fonts/KaTeX_Main-BoldItalic.woff 26.2 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/just-a-simple-lin-reg.png 26.6 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/heaviside-step-graph-2.png 26.9 kB
assets/css/fonts/KaTeX_Main-Italic.woff 27.2 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/img/gmm-1d-quiz.png 27.4 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-21-at-4.34.08-pm.png 27.5 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/24. Quiz Mini-batch.html 27.6 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/softmax.png 27.7 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/13. Implementing Gradient Descent.html 28.2 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/lin-reg-w-outliers.png 28.2 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-20-at-12.02.06-pm.png 28.3 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/06-l-supervised-classification-391-1.jpg 28.3 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/sigmoid.png 28.4 kB
Part 10-Module 01-Lesson 05_Interview Practice/img/8733666938.gif 28.5 kB
Part 10-Module 01-Lesson 05_Interview Practice/img/8733666942.gif 28.5 kB
Part 10-Module 01-Lesson 05_Interview Practice/img/8733666950.gif 28.5 kB
Part 10-Module 01-Lesson 05_Interview Practice/img/8733666934.gif 28.5 kB
Part 10-Module 01-Lesson 05_Interview Practice/img/8733666954.gif 28.5 kB
Part 10-Module 01-Lesson 05_Interview Practice/img/8733666946.gif 28.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/conv-dims.png 29.2 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/lin-reg-no-outliers.png 29.3 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/media/fxGOlnw9F9-fclp44Rh_TxDD_bAPzej25qdBqoXcIRYlrbiM722D-3k3WhbODeAxBVZpcCi1dCZsb7fB=s0#w=721&h=191 29.5 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/media/unnamed-135397-0.gif 29.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/pooling-dims.png 29.9 kB
assets/css/fonts/KaTeX_SansSerif-Regular.ttf 30.2 kB
assets/css/fonts/KaTeX_Main-Bold.woff2 30.6 kB
assets/css/fonts/KaTeX_SansSerif-Italic.ttf 31.3 kB
Part 01-Module 02-Lesson 01_Career Services Available to You/img/screen-shot-2018-07-27-at-1.24.38-pm.png 31.6 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/session.png 31.6 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/img/relu-network.png 31.8 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/roc-curve.png 32.2 kB
assets/css/fonts/KaTeX_Main-Regular.woff2 32.9 kB
assets/css/fonts/KaTeX_AMS-Regular.woff2 33.2 kB
Part 04-Module 04-Lesson 01_PCA/media/unnamed-134180-instructor-note-0.gif 33.6 kB
Part 04-Module 04-Lesson 01_PCA/media/GB13F-kVGVOcTVBqXIDUlthncR5O7h5RSarq_gp4sthoGuoXpI2dfcUthjiwuLdX9T_iK7W40gddelCmfg=s0#w=632&h=477 33.6 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/relu.png 33.9 kB
assets/css/fonts/KaTeX_SansSerif-Bold.ttf 34.0 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2018-01-08-at-5.37.22-am.png 34.0 kB
assets/css/fonts/KaTeX_Fraktur-Regular.ttf 34.7 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/screen-shot-2017-09-04-at-2.07.44-pm.png 34.9 kB
assets/css/fonts/KaTeX_Fraktur-Bold.ttf 36.0 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/grid-layer-1.png 36.1 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/grid-layer-1.png 36.1 kB
assets/css/fonts/KaTeX_Typewriter-Regular.ttf 36.3 kB
assets/css/fonts/KaTeX_Main-Bold.woff 36.8 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/img/semi-supervised-learning.jpg 37.7 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/example-before-bias.png 37.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/maxpool.jpeg 38.0 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/maxpool.jpeg 38.0 kB
Part 01-Module 02-Lesson 01_Career Services Available to You/img/udacitylogo-copy.png 38.6 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/local-minima.png 39.0 kB
assets/css/fonts/KaTeX_Main-Regular.woff 39.4 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hq-new-xor-table.png 39.5 kB
assets/css/fonts/KaTeX_Math-BoldItalic.ttf 39.7 kB
assets/css/fonts/KaTeX_AMS-Regular.woff 40.2 kB
assets/css/fonts/KaTeX_Math-Italic.ttf 41.4 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/aws-add-sec-group.png 42.7 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/img/eggsdata.png 42.8 kB
assets/css/jquery.mCustomScrollbar.min.css 42.8 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/cover-letter-intro-bad.png 43.3 kB
assets/css/fonts/KaTeX_Main-BoldItalic.ttf 44.8 kB
assets/js/jquery.mCustomScrollbar.concat.min.js 45.5 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/img/svm-image.png 46.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/layer-1-grid.png 46.8 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/layer-1-grid.png 46.8 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/sample-roc-curve.png 47.4 kB
assets/css/fonts/KaTeX_Main-Italic.ttf 48.0 kB
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/screen-shot-2018-04-14-at-3.13.15-pm.png 48.2 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/stop.png 48.7 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/screen-shot-2017-12-14-at-3.11.32-pm.png 49.1 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/multilayer-diagram-weights.png 49.7 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/simple-neuron.png 50.3 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/img/data.png 50.7 kB
assets/js/bootstrap.min.js 51.0 kB
Part 02-Module 03-Lesson 01_Model Selection/img/circle-data.png 51.1 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/img/circle-data.png 51.1 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/img/screen-shot-2018-01-06-at-8.13.20-pm.png 52.0 kB
Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-01-06-at-8.13.20-pm.png 52.0 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/input-times-weights.png 53.1 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/network-with-labeled-nodes.png 53.2 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-21-at-3.46.12-pm.png 53.5 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/softmax-input-output.png 53.7 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-25-at-9.18.00-pm.png 53.7 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/img/points.png 54.7 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/heirarchy-diagram.jpg 54.9 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/notmnist.png 55.5 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/derivative-example.png 56.4 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-21-at-3.25.10-pm.png 56.9 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/screen-shot-2017-10-17-at-11.02.44-am.png 57.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.49.08-pm.png 58.7 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/sigmoids.png 59.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/screen-shot-2017-10-04-at-2.46.11-pm.png 60.4 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/network-with-labeled-weights.png 60.9 kB
assets/css/fonts/KaTeX_Main-Bold.ttf 61.7 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/cover-letter-good-conclusion.png 63.8 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/cross-entropy-diagram.png 64.2 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/img/dropout-node.jpeg 64.2 kB
Part 05-Module 01-Lesson 01_Neural Networks/img/points.png 64.7 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/points.png 64.7 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/convolution-schematic.gif 65.2 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/convolution-schematic.gif 65.2 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-25-at-5.51.40-pm.png 66.1 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.50.54-pm.png 66.2 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/example-after-bias.png 67.3 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/cover-letter-intro-good.png 67.5 kB
Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-01-06-at-9.30.27-pm.png 68.0 kB
Part 06-Module 01-Lesson 01_Introduction to RL/img/paper-notes.svg.png 69.0 kB
Part 03-Module 01-Lesson 04_Naive Bayes/img/spam.png 69.4 kB
assets/css/fonts/KaTeX_Main-Regular.ttf 70.1 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/just-a-2d-reg.png 70.1 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/and-table.png 70.8 kB
assets/css/fonts/KaTeX_AMS-Regular.ttf 71.4 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/gradient-descent.png 73.7 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/img/enable-gpu.png 75.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/screen-shot-2017-10-12-at-5.47.45-pm.png 75.4 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/img/linear-boundary.png 77.0 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/img/gmm-2d-quiz.png 80.3 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-25-at-6.02.37-pm.png 80.7 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/roc.png 80.9 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/matrix-mult-3.png 80.9 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/img/polynomial-kernel-2-quiz.png 81.5 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/img/gmm-quiz.png 82.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/screen-shot-2017-10-05-at-3.55.40-pm.png 86.7 kB
assets/js/jquery-3.3.1.min.js 86.9 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/tensorflow.png 87.3 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/img/regularization-quiz.png 90.0 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/162524.gif 90.1 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/VeYoH8U6oDIhYrfUAGBaGscvxHIifRRNiptuYPpGfYtieCq3CUj1WjazsVq9HOSM4MwdG89rQE1I9lvbEQ=s0#w=762&h=455 90.1 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/img/screen-shot-2018-08-17-at-2.07.36-pm.png 93.4 kB
Part 03-Module 01-Lesson 03_Decision Trees/img/student-data.png 94.1 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/example-data.png 94.3 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hq-new-and-or-percep-fixed.png 94.8 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/perceptronquiz.png 95.9 kB
Part 05-Module 01-Lesson 01_Neural Networks/img/perceptronquiz.png 95.9 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/img/summary.png 96.0 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/xor-quiz.png 96.4 kB
Part 05-Module 01-Lesson 01_Neural Networks/img/xor-quiz.png 96.4 kB
Part 09-Module 01-Lesson 02_LinkedIn Review/media/R0A5rnKYyzLPZJ8B_pkyxdKkvab5qQi2LnEpFq2L-F33TSgzmjduHuUyDi-Z_ka2L7oU50UYqQTeU1n8VcM=s0#w=400&h=333 96.8 kB
Part 09-Module 01-Lesson 02_LinkedIn Review/media/unnamed-project-desc-0.gif 96.8 kB
Part 02-Module 03-Lesson 01_Model Selection/img/complexity.png 97.9 kB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/img/external-indices-quiz.png 98.8 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/img/kernel-trick.png 101.2 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/legend.png 104.5 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/article-2278590-1792e332000005dc-394-634x615.jpg 105.5 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/img/accuracy-quiz.png 108.4 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/img/nn.png 108.5 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/amazonwebservices-logo.svg.png 109.7 kB
Part 02-Module 03-Lesson 01_Model Selection/img/learning-curves.png 111.6 kB
Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-01-06-at-9.41.01-pm.png 113.4 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/backgammonboard.svg.png 115.5 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hq-perceptron.png 118.7 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/img/decision-trees.png 119.8 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/admissions-data.png 121.2 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/improve.png 127.4 kB
Part 04-Module 02-Lesson 01_Clustering/img/3058428551.gif 127.7 kB
assets/js/plyr.polyfilled.min.js 129.2 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/filter-depth.png 130.8 kB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/img/poker-hand-3-of-a-kind.png 131.7 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/img/screen-shot-2017-08-09-at-7.09.54-pm.png 132.0 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/p2xlarge-limit-request.png 132.8 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/sample-confusion-matrix.png 133.7 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2018-07-19-at-5.39.37-pm.png 134.2 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/img/screen-shot-2018-08-17-at-2.07.46-pm.png 137.3 kB
Part 03-Module 01-Lesson 04_Naive Bayes/img/spamham.png 138.3 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/minibatch.png 140.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/roc-curves.png 140.6 kB
assets/css/bootstrap.min.css 140.9 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/cover-letter-body-good.png 143.4 kB
Part 03-Module 01-Lesson 03_Decision Trees/img/recommending-apps.png 143.9 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/constant-alpha.png 147.1 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2017-11-26-at-10.30.15-am.png 148.6 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/img/parrot-ar-drone.jpg 150.0 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/img/email.png 152.1 kB
Part 04-Module 02-Lesson 01_Clustering/img/3040398570.gif 152.3 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/est-action.png 154.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/incremental.png 155.6 kB
Part 04-Module 04-Lesson 01_PCA/img/3062928590.gif 156.5 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-21-at-3.08.03-pm.png 156.6 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/sensitivity-specificity.png 158.9 kB
Part 04-Module 03-Lesson 01_Feature Scaling/img/3076888537.gif 160.3 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/precision-recall.png 160.5 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/screen-shot-2017-12-17-at-9.41.03-am.png 162.0 kB
Part 04-Module 04-Lesson 01_PCA/img/3059228570.gif 163.7 kB
Part 04-Module 02-Lesson 01_Clustering/img/3004978616.gif 168.5 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.49.43-pm.png 169.6 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/img/naive-bayes-quiz.png 170.4 kB
Part 04-Module 06-Lesson 01_Random Projection and ICA/img/eeg-ica.png 175.0 kB
Part 04-Module 02-Lesson 01_Clustering/img/3034378634.gif 177.3 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/quiz.jpg 178.4 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/2-card-21.png 180.1 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/mat-headshot.png 184.3 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/pup.jpg 185.6 kB
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/new-tab.gif 185.7 kB
Part 04-Module 02-Lesson 01_Clustering/img/3056738546.gif 188.1 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/1omsg2-mkguagky1c64uflw.gif 188.4 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/new-confusion-matrix.png 190.6 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/img/medical.png 191.0 kB
Part 04-Module 04-Lesson 01_PCA/img/2979238559.gif 191.5 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/img/screen-shot-2018-01-03-at-2.23.38-pm.png 192.4 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/p2-limit-increase.png 192.7 kB
Part 02-Module 01-Lesson 01_Training and Testing Models/img/curves.png 193.0 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/media/ZQfXMiez5ayPCZR0da9L4p9nNSKTsICaR9z-Bf9xkUJMTTmsDi1gTaIfLvgYNiNxwRUshpcdUPB-4l6CMWE=s0#w=581&h=678 193.3 kB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/media/unnamed-5101-0.gif 193.3 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/img/confusion.png 193.4 kB
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.mp4 193.4 kB
Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.mp4 193.9 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/img/7883232307.gif 194.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/media/monkey-doctor.png 194.5 kB
Part 10-Module 02-Lesson 05_Trees/img/7900766165.gif 195.3 kB
Part 03-Module 01-Lesson 03_Decision Trees/img/table.png 196.7 kB
Part 04-Module 02-Lesson 01_Clustering/img/3050028596.gif 196.8 kB
Part 04-Module 04-Lesson 01_PCA/img/3083018581.gif 199.8 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2017-06-13-at-12.58.03-pm.png 201.0 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/batch-stochastic.png 201.6 kB
Part 10-Module 02-Lesson 02_List-Based Collections/img/7890272657.gif 202.3 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hq-new-plot-perceptron-combine-v2.png 205.7 kB
Part 04-Module 02-Lesson 01_Clustering/img/3081768538.gif 207.7 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-21-at-12.20.30-pm.png 208.0 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-21-at-12.20.30-pm.png 208.0 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/exploration-vs.-exploitation.png 209.2 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/meme.png 214.1 kB
Part 03-Module 01-Lesson 03_Decision Trees/img/meme.png 214.1 kB
Part 03-Module 01-Lesson 04_Naive Bayes/img/meme.png 214.1 kB
Part 04-Module 02-Lesson 01_Clustering/img/meme.png 214.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/img/meme.png 214.1 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-21-at-12.20.50-pm.png 215.6 kB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/img/7889679710.gif 218.9 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/img/multi-layer.png 219.5 kB
Part 05-Module 01-Lesson 01_Neural Networks/img/xor.png 220.1 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/xor.png 220.1 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/img/margin-geometry-images.002.jpeg 220.6 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-02.png 224.5 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/screen-shot-2017-09-26-at-4.22.09-pm.png 224.6 kB
index.html 226.0 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/full-padding-no-strides-transposed.gif 227.1 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/dog-1210559-1280.jpg 228.3 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hq-new-plot-perceptron-combine.png 230.3 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/truncated-eval.png 230.6 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/img/margin-geometry-images.001.jpeg 231.0 kB
Part 04-Module 04-Lesson 01_PCA/img/3065198593.gif 233.4 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-09.png 233.5 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/img/recall-quiz.png 233.7 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-03.png 234.4 kB
Part 04-Module 02-Lesson 01_Clustering/img/2956218691.gif 235.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/cat-1.jpeg 236.3 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/cat-2.jpeg 236.8 kB
assets/js/katex.min.js 236.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-05.png 238.1 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/perceptron-graphics.001.jpeg 238.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-07.png 238.9 kB
Part 04-Module 03-Lesson 01_Feature Scaling/img/2981618588.gif 240.7 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/iteration.png 247.2 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-08.png 247.4 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-10.png 247.6 kB
Part 10-Module 02-Lesson 05_Trees/img/tree-traversal-practice.jpg 252.9 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/matengai-of-kuniga-coast-in-oki-island-shimane-pref600.jpg 252.9 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/img/precision-quiz.png 256.8 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-01.png 257.3 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/img/margin-geometry-images.003.jpeg 259.7 kB
Part 04-Module 04-Lesson 01_PCA/img/3095478574.gif 260.0 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/expected-sarsa.png 260.5 kB
Part 04-Module 04-Lesson 01_PCA/img/3059748569.gif 261.0 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-04.png 261.3 kB
Part 04-Module 03-Lesson 01_Feature Scaling/img/2967238555.gif 263.1 kB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/img/screen-shot-2018-06-12-at-5.07.10-pm.png 263.6 kB
Part 04-Module 08-Lesson 01_Creating Customer Segments/img/step-2-file-upload.png 264.5 kB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/img/step-2-file-upload.png 264.5 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/img/step-2-file-upload.png 264.5 kB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-06.png 265.3 kB
Part 04-Module 04-Lesson 01_PCA/img/3073008570.gif 265.4 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/policy-eval.png 265.9 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/screen-shot-2017-09-26-at-11.03.16-pm.png 265.9 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.mp4 266.2 kB
Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.mp4 266.2 kB
Part 04-Module 04-Lesson 01_PCA/img/3097488603.gif 268.1 kB
Part 04-Module 04-Lesson 01_PCA/img/3099598537.gif 269.1 kB
Part 04-Module 04-Lesson 01_PCA/img/3090048570.gif 269.3 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/sarsamax.png 270.9 kB
Part 05-Module 01-Lesson 01_Neural Networks/img/and-quiz.png 272.2 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/and-quiz.png 272.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/screen-shot-2017-10-04-at-5.01.26-pm.png 278.4 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/img/margin-geometry-images.004.jpeg 279.4 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/truncated-iter.png 280.6 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/mc-control-constant-a.png 281.6 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2018-01-08-at-5.38.03-am.png 282.8 kB
Part 04-Module 04-Lesson 01_PCA/img/2959748717.gif 282.8 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/vlcsnap-2016-11-24-15h52m47s438.png 287.0 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/img/margin-geometry-images.005.jpeg 288.1 kB
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.mp4 289.2 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/img/screen-shot-2018-01-06-at-10.44.48-pm.png 292.3 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/img/layers.png 293.0 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/sarsa.png 293.7 kB
Part 04-Module 04-Lesson 01_PCA/img/3094188555.gif 294.2 kB
Part 10-Module 02-Lesson 06_Graphs/media/unnamed-69567-0.gif 295.6 kB
Part 10-Module 02-Lesson 06_Graphs/img/7919804788.gif 295.6 kB
Part 10-Module 02-Lesson 06_Graphs/media/5gl2J73khhHQAERWImk7Y-GBP8onqRMMF5wIztkfj_8l8iT70qfBNIgUuaqS6Zoz1qUreJZA6PIMadm5ACc=s0#w=1920&h=1080 295.6 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/img/step1-file-upload.png 297.7 kB
Part 04-Module 08-Lesson 01_Creating Customer Segments/img/step1-file-upload.png 297.7 kB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/img/step1-file-upload.png 297.7 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/img/7881207114.gif 298.3 kB
Part 10-Module 02-Lesson 03_Searching and Sorting/img/7910014174.gif 304.2 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/mc-control-glie.png 304.3 kB
Part 03-Module 01-Lesson 03_Decision Trees/img/trees.png 307.2 kB
Part 04-Module 08-Lesson 01_Creating Customer Segments/img/step-0.png 309.2 kB
Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/img/step-0.png 309.2 kB
Part 03-Module 01-Lesson 08_Supervised Learning Project/img/step-0.png 309.2 kB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/a-b-c-fill-nn.png 312.8 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/img/all-ranks.png 315.9 kB
Part 04-Module 04-Lesson 01_PCA/img/2962878580.gif 316.5 kB
Part 06-Module 02-Lesson 02_Deep Q-Learning/img/atari-network.png 317.4 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/confusion-matrix.png 318.4 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/td-prediction.png 318.6 kB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/cover-letter-career-service-example.png 322.5 kB
Part 04-Module 04-Lesson 01_PCA/img/2946478670.gif 322.6 kB
Part 04-Module 04-Lesson 01_PCA/img/2966288580.gif 326.5 kB
Part 10-Module 02-Lesson 04_Maps and Hashing/img/7905614952.gif 333.3 kB
Part 04-Module 04-Lesson 01_PCA/img/3079068542.gif 335.5 kB
Part 04-Module 03-Lesson 01_Feature Scaling/img/2949288751.gif 336.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/teeth-whiskers-tongue.png 339.9 kB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/screen-shot-2017-12-17-at-12.49.34-pm.png 340.5 kB
Part 04-Module 04-Lesson 01_PCA/img/2985858609.gif 344.6 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/img/fbeta.png 345.2 kB
Part 04-Module 04-Lesson 01_PCA/img/2970968572.gif 345.2 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/vlcsnap-2016-11-24-16h01m35s262.png 349.5 kB
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.mp4 349.8 kB
Part 04-Module 04-Lesson 01_PCA/img/3075798615.gif 350.3 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/mc-pred-state.png 356.5 kB
Part 04-Module 04-Lesson 01_PCA/img/2963418671.gif 356.6 kB
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.mp4 359.1 kB
Part 04-Module 04-Lesson 01_PCA/img/2944258660.gif 363.4 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/mc-pred-action.png 372.3 kB
Part 03-Module 01-Lesson 05_Support Vector Machines/img/margin-geometry-images.008.jpeg 378.3 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/value-iteration.png 390.4 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/or-quiz.png 403.1 kB
Part 05-Module 01-Lesson 01_Neural Networks/img/or-quiz.png 403.1 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.mp4 404.5 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. Images-1GdiN5Wc8LA.mp4 404.9 kB
Part 04-Module 02-Lesson 01_Clustering/img/3013998667.gif 414.3 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/screen-shot-2017-09-26-at-2.18.38-pm.png 415.6 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2017-11-26-at-9.55.20-am.png 424.2 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/img/regularization-quiz.png 431.0 kB
Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.mp4 432.7 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/retriever-patch.png 446.0 kB
Part 04-Module 04-Lesson 01_PCA/img/2991788616.gif 449.8 kB
Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2017-11-26-at-9.38.24-am.png 451.5 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/retriever-patch-shifted.png 453.9 kB
Part 09-Module 02-Lesson 01_GitHub Review/img/6551597473.gif 455.0 kB
Part 09-Module 02-Lesson 01_GitHub Review/img/6499079068.gif 456.6 kB
Part 09-Module 02-Lesson 01_GitHub Review/img/6485174133.gif 469.1 kB
Part 04-Module 03-Lesson 01_Feature Scaling/img/3215618544.gif 471.6 kB
assets/img/udacimak.png 472.1 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-08-31-at-3.27.10-pm.png 474.2 kB
Part 04-Module 03-Lesson 01_Feature Scaling/img/3204388552.gif 474.8 kB
Part 04-Module 03-Lesson 01_Feature Scaling/img/3214548558.gif 479.0 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/threshold.png 479.5 kB
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.mp4 484.4 kB
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.mp4 495.5 kB
Part 03-Module 01-Lesson 01_Linear Regression/img/house.png 503.3 kB
Part 04-Module 03-Lesson 01_Feature Scaling/img/3204138549.gif 508.6 kB
Part 04-Module 03-Lesson 01_Feature Scaling/img/3219238538.gif 524.0 kB
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.mp4 559.2 kB
Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/img/submit-workspace.png 559.8 kB
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.mp4 570.0 kB
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.mp4 583.0 kB
Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.mp4 587.6 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/cat-3.png 589.7 kB
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.mp4 591.8 kB
Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.mp4 609.8 kB
Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.mp4 612.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/img/and-to-or.png 620.7 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/and-to-or.png 620.7 kB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/go.jpg 629.6 kB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-24-at-4.28.04-pm.png 637.6 kB
Part 02-Module 03-Lesson 01_Model Selection/img/models.png 643.0 kB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/actionvalue.png 643.5 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.mp4 647.1 kB
Part 03-Module 01-Lesson 06_Ensemble Methods/img/screen-shot-2018-01-03-at-2.20.30-pm.png 662.9 kB
Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.mp4 676.1 kB
Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.mp4 709.4 kB
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.mp4 719.4 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.mp4 725.9 kB
Part 09-Module 02-Lesson 01_GitHub Review/img/6509638772.gif 728.1 kB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/screen-shot-2017-10-04-at-4.58.58-pm.png 733.2 kB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.mp4 750.0 kB
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.mp4 763.2 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/student-quiz.png 767.0 kB
Part 05-Module 01-Lesson 01_Neural Networks/img/student-quiz.png 767.0 kB
Part 01-Module 02-Lesson 01_Career Services Available to You/img/get-hired-with-the-udacity-career-portal.gif 774.9 kB
Part 04-Module 02-Lesson 01_Clustering/img/sebastian-katie-jay.png 798.5 kB
Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.mp4 800.8 kB
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.mp4 823.0 kB
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/open-terminal.gif 838.9 kB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.mp4 839.5 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/15. Local Minima-gF_sW_nY-xw.mp4 839.5 kB
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.mp4 874.1 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.mp4 883.2 kB
Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.mp4 894.1 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.mp4 909.9 kB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/nature.png 914.5 kB
Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.mp4 949.3 kB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. XOR Perceptron-TF83GfjYLdw.mp4 969.7 kB
Part 05-Module 01-Lesson 01_Neural Networks/08. XOR Perceptron-TF83GfjYLdw.mp4 969.7 kB
Part 03-Module 01-Lesson 01_Linear Regression/05. Moving A Line-8EIHFyL2Log.mp4 1.0 MB
Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.mp4 1.0 MB
Part 03-Module 01-Lesson 01_Linear Regression/21. Polynomial Regression-DBhWG-PagEQ.mp4 1.0 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/logistic-regression-quiz.png 1.0 MB
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.mp4 1.0 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/statevalue.png 1.0 MB
Part 03-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.mp4 1.0 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.mp4 1.1 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/05. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.mp4 1.1 MB
Part 04-Module 06-Lesson 01_Random Projection and ICA/07. L6 5 ICA Implementation V1 V1-fZGxYfJmKaE.mp4 1.1 MB
Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.mp4 1.1 MB
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.mp4 1.1 MB
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.mp4 1.1 MB
Part 02-Module 01-Lesson 01_Training and Testing Models/02. 02 Intro SC V1-mIgABrjJVBY.mp4 1.1 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion-Matrix-Solution-ywwSzyU9rYs.mp4 1.2 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.mp4 1.2 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.mp4 1.2 MB
Part 03-Module 01-Lesson 01_Linear Regression/04. Fitting A Line-gkdoknEEcaI.mp4 1.2 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.mp4 1.2 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.mp4 1.2 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.mp4 1.2 MB
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.mp4 1.2 MB
Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.mp4 1.2 MB
Part 04-Module 06-Lesson 01_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.mp4 1.2 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.mp4 1.2 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.mp4 1.2 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/convolutionalnetworksquiz.png 1.2 MB
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.mp4 1.2 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/arch.png 1.3 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
Part 09-Module 01-Lesson 02_LinkedIn Review/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
Part 09-Module 02-Lesson 01_GitHub Review/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
Part 10-Module 01-Lesson 05_Interview Practice/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
Part 08-Module 01-Lesson 01_Conduct a Job Search/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training The Neural Network-HwiI-UXUx-M.mp4 1.3 MB
Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2-8nG8zzJMbZw.mp4 1.3 MB
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.mp4 1.3 MB
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.mp4 1.3 MB
Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.mp4 1.4 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.mp4 1.4 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.mp4 1.4 MB
Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.mp4 1.4 MB
Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.mp4 1.4 MB
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.mp4 1.4 MB
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.mp4 1.5 MB
Part 02-Module 03-Lesson 01_Model Selection/12. Outro SC V1-YD1grQje9fw.mp4 1.5 MB
Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.mp4 1.5 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.mp4 1.5 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.mp4 1.5 MB
Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.mp4 1.5 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. 06 Image Challenge V3-Efnoj1KNPHw.mp4 1.5 MB
Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.mp4 1.6 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.mp4 1.6 MB
Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4 1.6 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/img/frozen-lake-6.jpg 1.6 MB
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.mp4 1.6 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.mp4 1.6 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.mp4 1.6 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.mp4 1.6 MB
Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.mp4 1.6 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/lesions.png 1.6 MB
Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.mp4 1.6 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.mp4 1.7 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.mp4 1.7 MB
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.mp4 1.7 MB
Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.mp4 1.7 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees Question-1RonLycEJ34.mp4 1.7 MB
Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.mp4 1.7 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.mp4 1.7 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions Cont.-utOv-BKI_vo.mp4 1.7 MB
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.mp4 1.7 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.mp4 1.7 MB
Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.mp4 1.7 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/skin-disease-classes.png 1.7 MB
Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.mp4 1.7 MB
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.mp4 1.8 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.mp4 1.8 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4 1.8 MB
Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.mp4 1.8 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.mp4 1.8 MB
Part 10-Module 02-Lesson 05_Trees/13. BST Complications-pcB0wV7myy4.mp4 1.8 MB
Part 02-Module 03-Lesson 01_Model Selection/04. KFold Cross Validation V3 V1-9W6o6eWGi-0.mp4 1.8 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.mp4 1.8 MB
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.mp4 1.9 MB
Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.mp4 1.9 MB
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.mp4 1.9 MB
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.mp4 1.9 MB
Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.mp4 1.9 MB
Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.mp4 2.0 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.mp4 2.0 MB
Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.mp4 2.0 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.mp4 2.0 MB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.mp4 2.0 MB
Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.mp4 2.1 MB
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.mp4 2.1 MB
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/run-main.gif 2.1 MB
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.mp4 2.1 MB
Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.mp4 2.1 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.mp4 2.2 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.mp4 2.2 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.mp4 2.2 MB
Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.mp4 2.2 MB
Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.mp4 2.2 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.mp4 2.2 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.mp4 2.2 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.mp4 2.2 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/09. 07 Recall SC V1-0n5wUZiefkQ.mp4 2.3 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/05. When Accuracy Wont Work-r0-O-gIDXZ0.mp4 2.3 MB
Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.mp4 2.3 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.mp4 2.3 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.mp4 2.3 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.mp4 2.3 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.mp4 2.3 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.mp4 2.3 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Answer False Negatives And Positives-KOytJL1lvgg.mp4 2.3 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.mp4 2.3 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/08. 06 Precision SC V1-q2wVorBfefU.mp4 2.4 MB
Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.mp4 2.4 MB
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.mp4 2.4 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.mp4 2.4 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.mp4 2.4 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.mp4 2.4 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.mp4 2.4 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.mp4 2.4 MB
Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.mp4 2.4 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.mp4 2.4 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.mp4 2.5 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.mp4 2.5 MB
Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.mp4 2.5 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.mp4 2.5 MB
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.mp4 2.5 MB
Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.mp4 2.5 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.mp4 2.5 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.mp4 2.5 MB
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.mp4 2.6 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.mp4 2.6 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.mp4 2.6 MB
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.mp4 2.6 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.mp4 2.6 MB
Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.mp4 2.6 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.mp4 2.6 MB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.mp4 2.6 MB
Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.mp4 2.6 MB
Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.mp4 2.7 MB
Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.mp4 2.7 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.mp4 2.7 MB
Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.mp4 2.7 MB
Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4 2.7 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.mp4 2.7 MB
Part 03-Module 01-Lesson 04_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.mp4 2.7 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.mp4 2.7 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.mp4 2.8 MB
Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.mp4 2.8 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/11. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.mp4 2.8 MB
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.mp4 2.8 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.8 MB
Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.8 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.mp4 2.8 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.mp4 2.9 MB
Part 02-Module 01-Lesson 01_Training and Testing Models/01. 01 Intro-4C4PuJANIdE.mp4 2.9 MB
Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/open-agent-monitor-main.gif 2.9 MB
Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.mp4 2.9 MB
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.mp4 2.9 MB
Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.mp4 2.9 MB
Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.mp4 2.9 MB
Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.mp4 2.9 MB
Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.mp4 2.9 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.mp4 2.9 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.mp4 2.9 MB
Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.mp4 2.9 MB
Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.mp4 3.0 MB
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.mp4 3.0 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 3.0 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.mp4 3.0 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.mp4 3.0 MB
Part 03-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution-G3fRVgLa5gI.mp4 3.0 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.mp4 3.0 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.mp4 3.0 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.mp4 3.0 MB
Part 05-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 3.0 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 3.0 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.08.11-pm.png 3.0 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.08.11-pm.png 3.0 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.mp4 3.0 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.mp4 3.1 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.mp4 3.1 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.mp4 3.1 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.mp4 3.1 MB
Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.mp4 3.1 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/06. M2L3 06 V1-RMjdQkl6CqE.mp4 3.2 MB
Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4 3.2 MB
Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.mp4 3.2 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.mp4 3.2 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.mp4 3.2 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.02-pm.png 3.2 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.02-pm.png 3.2 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.mp4 3.2 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.mp4 3.2 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.mp4 3.2 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.mp4 3.3 MB
Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.mp4 3.3 MB
Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.mp4 3.3 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.mp4 3.3 MB
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.mp4 3.3 MB
Part 05-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.4 MB
Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.mp4 3.4 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.mp4 3.4 MB
Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.mp4 3.4 MB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.mp4 3.4 MB
Part 03-Module 01-Lesson 01_Linear Regression/07. Square Trick-AGZEq-yQgRM.mp4 3.4 MB
Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.mp4 3.5 MB
Part 05-Module 01-Lesson 01_Neural Networks/29. Neural Networks Outro V2-pwA5shUkRVc.mp4 3.5 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 3.5 MB
Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.mp4 3.5 MB
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.mp4 3.5 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression-Metrics-906P4BPnl9A.mp4 3.5 MB
Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.mp4 3.5 MB
Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.mp4 3.5 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.mp4 3.5 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.mp4 3.6 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.mp4 3.6 MB
Part 02-Module 03-Lesson 01_Model Selection/08. Grid Search SC V1-zDw-ZGiHW5I.mp4 3.6 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.mp4 3.6 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/24. Neural Network Regression-aUJCBqBfEnI.mp4 3.6 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.mp4 3.6 MB
Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.mp4 3.6 MB
Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.mp4 3.6 MB
Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.mp4 3.7 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.24-pm.png 3.7 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.24-pm.png 3.7 MB
Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.mp4 3.7 MB
Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.mp4 3.7 MB
Part 03-Module 01-Lesson 06_Ensemble Methods/06. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.mp4 3.7 MB
Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.mp4 3.8 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.mp4 3.8 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.mp4 3.8 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.mp4 3.8 MB
Part 05-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.8 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.8 MB
Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.mp4 3.9 MB
Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.mp4 3.9 MB
Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.mp4 3.9 MB
Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.mp4 3.9 MB
Part 05-Module 01-Lesson 01_Neural Networks/24. Gradient Descent-rhVIF-nigrY.mp4 3.9 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.mp4 4.0 MB
Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4 4.0 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.mp4 4.0 MB
Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4 4.0 MB
Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.mp4 4.0 MB
Part 03-Module 01-Lesson 01_Linear Regression/11. Minimizing Error Functions-RbT2TXN_6tY.mp4 4.0 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1-NBWbluSbxPg.mp4 4.0 MB
Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.mp4 4.1 MB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.mp4 4.1 MB
Part 03-Module 01-Lesson 01_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.mp4 4.1 MB
Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.mp4 4.1 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.mp4 4.1 MB
Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.mp4 4.1 MB
Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.mp4 4.1 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4 4.1 MB
Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.mp4 4.2 MB
Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.mp4 4.2 MB
Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.mp4 4.2 MB
Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4 4.2 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.mp4 4.2 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.mp4 4.3 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.mp4 4.3 MB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.mp4 4.3 MB
Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.mp4 4.3 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.mp4 4.3 MB
Part 05-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4 4.3 MB
Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.mp4 4.4 MB
Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.mp4 4.4 MB
Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.mp4 4.4 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.mp4 4.4 MB
Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.mp4 4.4 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.mp4 4.4 MB
Part 05-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.mp4 4.4 MB
Part 03-Module 01-Lesson 01_Linear Regression/08. Gradient Descent-4s4x9h6AN5Y.mp4 4.5 MB
Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.mp4 4.5 MB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa-kEKupCyU0P0.mp4 4.5 MB
Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.mp4 4.5 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.mp4 4.5 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.mp4 4.6 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.mp4 4.6 MB
Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.mp4 4.7 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer-L5QBqYDNJn0.mp4 4.7 MB
Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.mp4 4.8 MB
Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.mp4 4.8 MB
Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.mp4 4.8 MB
Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.mp4 4.9 MB
Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.mp4 4.9 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.mp4 4.9 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.mp4 5.0 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.mp4 5.0 MB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.mp4 5.0 MB
Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4 5.0 MB
Part 05-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.mp4 5.1 MB
Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.mp4 5.1 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.mp4 5.1 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.mp4 5.2 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.mp4 5.2 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.mp4 5.2 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/01. M2L3 01 V1-YOSREyp04HA.mp4 5.2 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.mp4 5.2 MB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.mp4 5.3 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.3 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.3 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.mp4 5.3 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.4 MB
Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.4 MB
Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.mp4 5.4 MB
Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.mp4 5.4 MB
Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.mp4 5.4 MB
Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.mp4 5.4 MB
Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.mp4 5.4 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.mp4 5.5 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions-llakAjwox_8.mp4 5.5 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 5.6 MB
Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.6 MB
Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.mp4 5.6 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.mp4 5.7 MB
Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.mp4 5.7 MB
Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.mp4 5.7 MB
Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.mp4 5.7 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.mp4 5.7 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.mp4 5.7 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.mp4 5.8 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.mp4 5.8 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.mp4 5.8 MB
Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.mp4 5.9 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.mp4 5.9 MB
Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing-gmxGRJSKEb0.mp4 5.9 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 6.0 MB
Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4 6.0 MB
Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.mp4 6.0 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.mp4 6.1 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.mp4 6.1 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.mp4 6.1 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.mp4 6.2 MB
Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.mp4 6.3 MB
Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.mp4 6.3 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality-j231aRV74QM.mp4 6.3 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.mp4 6.3 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.mp4 6.3 MB
Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves SC V1-ZNhnNVKl8NM.mp4 6.3 MB
Part 04-Module 06-Lesson 01_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.mp4 6.3 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.mp4 6.3 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/10. 08 F1 Score SC V1-TRzBeL07fSg.mp4 6.3 MB
Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.mp4 6.4 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.mp4 6.4 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.mp4 6.4 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.mp4 6.4 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.mp4 6.5 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.mp4 6.5 MB
Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.mp4 6.6 MB
Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.mp4 6.6 MB
Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.mp4 6.6 MB
Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.mp4 6.6 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer-dRFd6HaAXys.mp4 6.7 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.mp4 6.7 MB
Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.mp4 6.8 MB
Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.mp4 6.8 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.mp4 6.8 MB
Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.mp4 6.9 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions-KJLaRfOOPGA.mp4 6.9 MB
Part 05-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4 6.9 MB
Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.mp4 7.0 MB
Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.mp4 7.0 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. ROC Curve-2Iw5TiGzJI4.mp4 7.0 MB
Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve-2Iw5TiGzJI4.mp4 7.0 MB
Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.mp4 7.0 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.mp4 7.1 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.mp4 7.1 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.mp4 7.1 MB
Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.mp4 7.2 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2-CUTtQvxKkNw.mp4 7.2 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.mp4 7.2 MB
Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.mp4 7.2 MB
Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.mp4 7.3 MB
Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.mp4 7.3 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.mp4 7.3 MB
Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.mp4 7.3 MB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.mp4 7.3 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.mp4 7.3 MB
Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.mp4 7.4 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.mp4 7.4 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.mp4 7.4 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.mp4 7.5 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.mp4 7.6 MB
Part 05-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.6 MB
Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.mp4 7.6 MB
Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.mp4 7.6 MB
Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.mp4 7.6 MB
Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.mp4 7.7 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.mp4 7.7 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.mp4 7.7 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.mp4 7.7 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.mp4 7.8 MB
Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.9 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/chess-game.jpg 7.9 MB
Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.mp4 7.9 MB
Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.mp4 8.0 MB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.mp4 8.0 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.mp4 8.0 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.mp4 8.0 MB
Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.mp4 8.1 MB
Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.mp4 8.1 MB
Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.mp4 8.1 MB
Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.mp4 8.2 MB
Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-2HY0Yr5FRn0.mp4 8.2 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.mp4 8.3 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.mp4 8.3 MB
Part 04-Module 06-Lesson 01_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.mp4 8.3 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.mp4 8.4 MB
Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.mp4 8.4 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.mp4 8.4 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.mp4 8.4 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4 8.5 MB
Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.mp4 8.5 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.mp4 8.5 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.mp4 8.6 MB
Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.mp4 8.6 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.mp4 8.6 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.mp4 8.6 MB
Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.mp4 8.7 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.mp4 8.8 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.mp4 8.8 MB
Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks-ISHGyvsT0QY.mp4 8.8 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.mp4 8.9 MB
Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.mp4 8.9 MB
Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Mat HS-9P7UPWFu8w8.mp4 8.9 MB
Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.mp4 8.9 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.mp4 9.1 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.mp4 9.1 MB
Part 03-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.mp4 9.2 MB
Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.mp4 9.3 MB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.mp4 9.3 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.mp4 9.3 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.mp4 9.4 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.mp4 9.5 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.mp4 9.5 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples & Applications MAIN V1 V2-HTahFoQwk2g.mp4 9.6 MB
Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.mp4 9.6 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.mp4 9.6 MB
Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.mp4 9.6 MB
Part 04-Module 06-Lesson 01_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.mp4 9.6 MB
Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4 9.7 MB
Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.mp4 9.7 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.mp4 9.7 MB
Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.mp4 9.7 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.mp4 9.7 MB
Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.mp4 9.8 MB
Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.mp4 9.9 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.mp4 9.9 MB
Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.mp4 9.9 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.9 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.9 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.9 MB
Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.mp4 10.1 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.mp4 10.2 MB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.mp4 10.2 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.mp4 10.2 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks-xFu1_2K2D2U.mp4 10.2 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.mp4 10.3 MB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.mp4 10.3 MB
Part 04-Module 06-Lesson 01_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.mp4 10.4 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.mp4 10.4 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.mp4 10.4 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.mp4 10.5 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.mp4 10.5 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.mp4 10.6 MB
Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team-cuKecPpZ7PM.mp4 10.6 MB
Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.mp4 10.7 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.mp4 10.8 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.mp4 10.8 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.mp4 10.8 MB
Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.mp4 10.8 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.mp4 10.9 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.mp4 10.9 MB
Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.mp4 11.0 MB
Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.mp4 11.0 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.mp4 11.2 MB
Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.mp4 11.3 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.mp4 11.6 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent-Math-7sxA5Ap8AWM.mp4 11.8 MB
Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.mp4 12.1 MB
Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.mp4 12.1 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 12.1 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 12.1 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 12.1 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.mp4 12.3 MB
Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.mp4 12.4 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.mp4 12.5 MB
Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.mp4 12.7 MB
Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.mp4 12.7 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.mp4 12.8 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.mp4 12.8 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.mp4 12.8 MB
Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.mp4 12.8 MB
Part 03-Module 01-Lesson 03_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4 12.9 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.mp4 13.1 MB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.mp4 13.1 MB
Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.mp4 13.1 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.mp4 13.2 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.mp4 13.2 MB
Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy-piLpj1V1HEk.mp4 13.2 MB
Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.mp4 13.2 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 13.3 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 13.3 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 13.3 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.mp4 13.3 MB
Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.mp4 13.3 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.mp4 13.5 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.mp4 13.5 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.mp4 13.5 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.mp4 13.6 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.mp4 13.6 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.mp4 13.7 MB
Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.mp4 13.8 MB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.mp4 13.8 MB
Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4 13.8 MB
Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.mp4 13.8 MB
Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.9 MB
Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.9 MB
Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.9 MB
Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.mp4 13.9 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.mp4 14.0 MB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.mp4 14.1 MB
Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.mp4 14.4 MB
Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.mp4 14.5 MB
Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.mp4 14.7 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.mp4 14.8 MB
Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.mp4 15.0 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.mp4 15.0 MB
Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.mp4 15.1 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.mp4 15.1 MB
Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.mp4 15.1 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.mp4 15.3 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.mp4 15.4 MB
Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.mp4 15.5 MB
Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.mp4 15.6 MB
Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks-Mqogpnp1lrU.mp4 15.6 MB
Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.mp4 15.7 MB
Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.mp4 15.9 MB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.mp4 16.0 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.mp4 16.2 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.mp4 16.4 MB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.mp4 16.9 MB
Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.mp4 17.2 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.mp4 17.3 MB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax-4DxoYuR7aZ4.mp4 17.3 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.mp4 17.4 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.mp4 17.5 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.mp4 17.5 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.mp4 17.5 MB
Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.mp4 17.7 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.mp4 17.8 MB
Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-r7g0Z-54vg0.mp4 17.9 MB
Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.mp4 18.0 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.mp4 18.2 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.mp4 18.2 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.mp4 18.3 MB
Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.mp4 18.4 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.mp4 18.6 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples & Applications MAIN V1 V2-GhyFsjQ4FkA.mp4 18.6 MB
Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.mp4 19.0 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.mp4 19.0 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.mp4 19.0 MB
Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.mp4 19.1 MB
Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.mp4 19.3 MB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.mp4 19.3 MB
Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.mp4 19.3 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.mp4 19.5 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.mp4 19.7 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/03. M2L3 03 V2-TePX-0Bs23E.mp4 19.8 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.mp4 20.7 MB
Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.mp4 20.8 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.mp4 20.9 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.mp4 20.9 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.mp4 21.0 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.mp4 21.1 MB
Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.mp4 21.2 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.mp4 21.6 MB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.mp4 21.7 MB
Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.mp4 21.7 MB
Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.mp4 21.8 MB
Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.mp4 21.9 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.mp4 22.0 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/04. M2L3 04 V1-QicxmyE5vTo.mp4 22.0 MB
Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.mp4 22.1 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.mp4 22.4 MB
Part 03-Module 01-Lesson 03_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4 22.7 MB
Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.mp4 22.8 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.mp4 23.1 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.mp4 23.1 MB
Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.mp4 23.3 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.mp4 23.6 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.mp4 24.3 MB
Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.mp4 24.4 MB
Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.mp4 24.5 MB
Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.mp4 25.9 MB
Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.mp4 26.3 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.mp4 26.9 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.mp4 27.6 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.mp4 27.9 MB
Part 03-Module 01-Lesson 05_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.mp4 28.1 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.mp4 28.9 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.mp4 30.1 MB
Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0)-CsD6b0csU7o.mp4 31.6 MB
Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.mp4 31.9 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.mp4 32.6 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.mp4 33.2 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.mp4 33.2 MB
Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.mp4 34.0 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/02. M2L3 02 V2-ToS8vXGdODE.mp4 34.1 MB
Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.mp4 34.1 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.mp4 34.2 MB
Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-jjdbGD4CBGk.mp4 34.3 MB
Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.mp4 34.8 MB
Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.mp4 35.0 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.mp4 41.3 MB
Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.mp4 42.7 MB
Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.mp4 42.7 MB
Part 06-Module 02-Lesson 03_Policy-Based Methods/07. M2L3 07 V2-ZBLLGIN1EfU.mp4 45.7 MB
Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-B3w_msqHP68.mp4 46.3 MB
Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.mp4 50.7 MB
Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-RyThtU8GcT0.mp4 51.2 MB
Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.mp4 52.2 MB
Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.mp4 66.7 MB
Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-ooqFCXMdxys.mp4 72.1 MB
Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-bXpONCq5ePE.mp4 77.9 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/08. Coding 2-qEteyPNRSwU.mp4 109.7 MB
Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.mp4 110.1 MB

==查看完整文档列表==

上一个：btra16752-480p.mp4427.2 MB
下一个：[Mac OS X] Adobe Premiere Pro CC 2018 v12.0.0.225 (Patched) 819.5 MB

24小时热门磁力链接

Andor [HDTV 720p][Cap.205] 8145次下载 Cap HDTV 720p
Маня и Груня.2024.WEB-DL 720p.Files-x 5109次下载 2024 WEB DL
Havoc.2025.1080p.NF.WEB-DL.DDP5.1.Atmos.H.264-EniaHD.mkv 5077次下载 WEB DL DDP5
The.Super.Cube.S01E09.1080p.iQ.WEB-DL.AAC2.0.H.264-VARYG.mkv 5040次下载 WEB DL Cube
Star.Wars.Andor.S02E05.I.Have.Friends.Everywhere.1080p.DS... 4944次下载 WEB DL Star
新桥恋人.电影港地址发布页 www.dygang.me 收藏不迷路 4365次下载 me www 发布页
Night at the Museum Battle of the Smithsonian 2009 2160p... 4161次下载 KiNGDOM WEB DL
www.UIndex.org - ... 4126次下载 www MeGusta HEVC
Palma.2.2024.WEB-DL.1080p.ExKinoRay.mkv 4000次下载 WEB DL 2.2024
Guns N' Roses - Greatest Hits Live Broadcast Collection... 3915次下载 Live Remastered Hits

本周热门磁力链接

Andor [HDTV 720p][Cap.205] 8145次下载 Cap HDTV 720p
(同人CG集) [白色絵の具 (無味ムスミ)] 友達の母が巨乳で金髪の淫乱美女でした.zip 6346次下载 zip 巨乳 CG
You.S05E02.720p.HEVC.x265-MeGusta[EZTVx.to].mkv 6221次下载 S05E02 HEVC 720p
up-mod-minecraft-play-with-friends-v1-21-80-28-... 6023次下载 982108028 mod play
the.last.of.us.s02e02.1080p.web.h264-successful... 5335次下载 web last h264
异世降临.6v电影地址发布页 www.6v123.net 收藏不迷路 5220次下载 www 发布页 6v123
Opasnaya.blizost.S01.2025.WEB-DL.1080p 5127次下载 WEB DL S01
Маня и Груня.2024.WEB-DL 720p.Files-x 5109次下载 2024 WEB DL
Andor.S02E02.Sagrona.Teema.1080p.HEVC.x265-MeGu... 5100次下载 HEVC Andor mkv
Havoc.2025.1080p.NF.WEB-DL.DDP5.1.Atmos.H.264-E... 5077次下载 WEB DL DDP5

种子标签

Nanodegree 0.0 Machine v1 Udacity Learning nd009t Engineer

种子评价

优质的种子 (0) 假种子 (0) 有密码 (0) 低质量 (0) 有病毒 (0) 无法下载 (0)
欢迎对种子质量进行评价。

人气女优

北川ゆい Akira COCOLO Saiko あいだももあさのくるみあまいれもんいしかわ愛里いとうしいなうさみ恭香うちだまひろかぐやひめかとりこのみかないかほくすのき琴美クミコグレースくらもとまい（葉月ありさ）さとみ中村あみしいな純菜しのざきさとみ（三沢亜也）牧本千幸（つかもと友希）眞木ありさデヴィテラパトリックドミニカともさかまいともさか愛なごみもえひなこ

分享BT种子/磁力链接

亲，你知道吗？下载的人越多速度越快，赶快把本页面分享给好友一起下载吧^_^

磁力管家