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【压缩文件】 microsoft-visual-studio-.-net-2003-prerequisites

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Microsoft Visual Studio .NET 2003 Prerequisites.iso 169.7 MB
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【影视】 [GigaCourse.com] Udemy - Deep Learning Prerequisites Logistic Regression in Python

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7. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
2. Basics What is linear classification What's the relation to neural networks/3. How do we calculate the output of a neuron logistic classifier - Theory.srt 84.1 MB
7. Appendix FAQ/10. Proof that using Jupyter Notebook is the same as not using it.srt 82.1 MB
7. Appendix FAQ/10. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
4. Practical concerns/11. Practical Section Summary.srt 82.1 MB
1. Start Here/1. Introduction and Outline.mp4 49.2 MB
7. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
7. Appendix FAQ/9. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
7. Appendix FAQ/14. BONUS Where to get discount coupons and FREE deep learning material.mp4 39.7 MB
7. Appendix FAQ/13. What order should I take your courses in (part 2).mp4 39.4 MB
7. Appendix FAQ/12. What order should I take your courses in (part 1).mp4 30.7 MB
2. Basics What is linear classification What's the relation to neural networks/5. Interpretation of Logistic Regression Output.mp4 29.2 MB
3. Solving for the optimal weights/7. Maximizing the likelihood.mp4 26.4 MB
4. Practical concerns/8. The donut problem.mp4 25.9 MB
7. Appendix FAQ/5. How to Code by Yourself (part 1).mp4 25.7 MB
6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp4 25.2 MB
4. Practical concerns/10. Why Divide by Square Root of D.mp4 24.6 MB
7. Appendix FAQ/2. Gradient Descent Tutorial.mp4 23.9 MB
6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp4 22.5 MB
3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.mp4 17.9 MB

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【压缩文件】 [DesireCourse.Net] Udemy - Deep Learning Prerequisites Logistic Regression in Python

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[DesireCourse.Net] Udemy - Deep Learning Prerequisites Logistic Regression in Python.zip 774.8 MB
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6. Setting Up Your Environment (FAQ by Student Request)/1. Anaconda Environment Setup.mp4 195.3 MB
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
1. Welcome/3. Statistics vs. Machine Learning.mp4 58.2 MB
6. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
1. Welcome/2. How to Succeed in this Course.mp4 46.0 MB
1. Welcome/1. Introduction and Outline.mp4 44.8 MB
8/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.8 MB
9. Appendix FAQ Finale/2. BONUS.mp4 39.7 MB
8/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 39.4 MB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 37.8 MB
8/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 30.7 MB
2. 1-D Linear Regression Theory and Code/4. Define the model in 1-D, derive the solution.mp4 25.9 MB
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp4 25.7 MB
4. Practical machine learning issues/17. Why Divide by Square Root of D.mp4 24.6 MB
4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23.9 MB
2. 1-D Linear Regression Theory and Code/11. Moore's Law Derivation.mp4 21.2 MB
2. 1-D Linear Regression Theory and Code/3. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB
8/1. How to Succeed in this Course (Long Version).mp4 19.2 MB
2. 1-D Linear Regression Theory and Code/10. Demonstrating Moore's Law in Code.mp4 18.3 MB
4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.mp4 18.1 MB

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【影视】 [Tutorialsplanet.NET] Udemy - Deep Learning Prerequisites Logistic Regression in Python

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8/1. Anaconda Environment Setup.mp4 195.3 MB
9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
1. Start Here/3. Statistics vs. Machine Learning.mp4 58.4 MB
8/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
1. Start Here/2. How to Succeed in this Course.mp4 45.9 MB
1. Start Here/1. Introduction and Outline.mp4 41.3 MB
10/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
11. Appendix FAQ Finale/2. BONUS.srt 39.7 MB
11. Appendix FAQ Finale/2. BONUS.mp4 39.7 MB
10/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 39.4 MB
10/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 30.7 MB
2/5. Interpretation of Logistic Regression Output.mp4 29.3 MB
3. Solving for the optimal weights/7. Maximizing the likelihood.mp4 26.4 MB
4. Practical concerns/8. The donut problem.mp4 25.9 MB
9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 1).mp4 25.7 MB
6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp4 25.2 MB
4. Practical concerns/10. Why Divide by Square Root of D.mp4 24.6 MB
7. Background Review/1. Gradient Descent Tutorial.mp4 23.9 MB
6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp4 22.5 MB
3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.mp4 17.9 MB

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6. Appendix/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
6. Appendix/11. What order should I take your courses in (part 2).mp4 85.6 MB
6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
6. Appendix/11. What order should I take your courses in (part 2).vtt 39.5 MB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 37.8 MB
1. Welcome/1. Welcome.mp4 33.6 MB
6. Appendix/10. What order should I take your courses in (part 1).mp4 30.7 MB
1. Welcome/2. Introduction and Outline.mp4 29.8 MB
2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 25.9 MB
6. Appendix/5. How to Code by Yourself (part 1).mp4 25.7 MB
4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23.9 MB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB
6. Appendix/7. How to Succeed in this Course (Long Version).mp4 19.2 MB
2. 1-D Linear Regression Theory and Code/7. Demonstrating Moore's Law in Code.mp4 18.3 MB
4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.mp4 18.1 MB
6. Appendix/12. Python 2 vs Python 3.mp4 17.7 MB
3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).mp4 17.2 MB
3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.mp4 15.6 MB

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【压缩文件】 [ FreeCourseWeb.com ] Deep Learning Prerequisites - Linear Regression in Python (Update).zip

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[ FreeCourseWeb.com ] Deep Learning Prerequisites - Linear Regression in Python (Update).zip 1.0 GB

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【影视】 [FreeTutorials.Us] deep-learning-prerequisites-the-numpy-stack-in-python

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01 Introduction and Outline/quizzes/001 How will you practice what youve learned in this course.html 3.7 kB
01 Introduction and Outline/001 Whats this course about How can you succeed What should you know first.mp4 10.5 MB
01 Introduction and Outline/002 Where to get the code and how to install libraries.mp4 7.0 MB
02 Numpy/003 Lists vs. Arrays.mp4 12.4 MB
02 Numpy/004 Dot product 1 For loop vs. cosine method vs. dot function.mp4 9.2 MB
02 Numpy/005 Dot product 2 Speed comparison.mp4 2.9 MB
02 Numpy/006 Vectors and Matrices.mp4 6.7 MB
02 Numpy/007 Generating Matrices to Work With.mp4 8.3 MB
02 Numpy/008 Matrix Products.mp4 4.6 MB
02 Numpy/009 More Matrix Operations.mp4 13.2 MB
02 Numpy/010 Solving a Linear System.mp4 4.2 MB
02 Numpy/011 Word Problem.mp4 3.1 MB
03 Pandas/012 Manual Data Loading.mp4 9.5 MB
03 Pandas/013 DataFrames.mp4 7.5 MB
03 Pandas/014 More about DataFrames Selecting Rows and Columns.mp4 9.6 MB
03 Pandas/015 Even More about DataFrames Column Names.mp4 6.3 MB
03 Pandas/016 The apply Function.mp4 5.9 MB
03 Pandas/017 Joins.mp4 4.7 MB
04 Matplotlib/018 Line Chart.mp4 6.6 MB
04 Matplotlib/019 Scatterplot.mp4 7.4 MB

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【压缩文件】 [ FreeCourseWeb.com ] Udemy - Machine Learning Prerequisites for 2021.zip

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[ FreeCourseWeb.com ] Udemy - Machine Learning Prerequisites for 2021.zip 485.3 MB

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【影视】 [Tutorialsplanet.NET] Udemy - Deep Learning Prerequisites Linear Regression in Python

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6. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.mp4 195.3 MB
1. Welcome/4. Anyone Can Succeed in this Course.mp4 88.1 MB
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
1. Welcome/5. Statistics vs. Machine Learning.mp4 58.2 MB
1. Welcome/1. Welcome.mp4 52.1 MB
6. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
8/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.8 MB
9. Appendix FAQ Finale/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
8/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 39.4 MB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 37.8 MB
8/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 30.7 MB
2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 25.9 MB
7. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp4 25.7 MB
4. Practical machine learning issues/17. Why Divide by Square Root of D.mp4 24.6 MB
4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23.9 MB
2. 1-D Linear Regression Theory and Code/9. Moore's Law Derivation.mp4 21.2 MB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB
8/1. How to Succeed in this Course (Long Version).mp4 19.2 MB
2. 1-D Linear Regression Theory and Code/8. Demonstrating Moore's Law in Code.mp4 18.4 MB
4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.mp4 18.1 MB

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【影视】 [ DevCourseWeb.com ] Udemy - Prerequisites to Machine Learning - A Beginners Guide

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~Get Your Files Here !/2. Python for Machine Learning/3. Range, Lists and Tuples.mp4 80.6 MB
~Get Your Files Here !/2. Python for Machine Learning/2. Functions.mp4 61.9 MB
~Get Your Files Here !/3. Packages and Statistics for Machine Learning/6. Mean, Median and Mode.mp4 43.6 MB
~Get Your Files Here !/3. Packages and Statistics for Machine Learning/5. Matplotlib Continue....mp4 42.3 MB
~Get Your Files Here !/3. Packages and Statistics for Machine Learning/7. Variance and Standard Deviation.mp4 40.1 MB
~Get Your Files Here !/3. Packages and Statistics for Machine Learning/1. NumPy.mp4 39.1 MB
~Get Your Files Here !/3. Packages and Statistics for Machine Learning/4. Matplotlib.mp4 36.4 MB
~Get Your Files Here !/2. Python for Machine Learning/4. Importing Modules.mp4 32.0 MB
~Get Your Files Here !/3. Packages and Statistics for Machine Learning/3. Read CSV File.mp4 31.9 MB
~Get Your Files Here !/2. Python for Machine Learning/1. Print Statement and Variables.mp4 27.2 MB
~Get Your Files Here !/3. Packages and Statistics for Machine Learning/2. Pandas.mp4 26.2 MB
~Get Your Files Here !/1. Introduction/4. Google Colab Notebook.mp4 23.6 MB
~Get Your Files Here !/1. Introduction/3. Introduction to Machine Learning.mp4 15.3 MB
~Get Your Files Here !/1. Introduction/1. Course Overview.mp4 6.7 MB
~Get Your Files Here !/3. Packages and Statistics for Machine Learning/5.1 matplotlib.ipynb 44.2 kB
~Get Your Files Here !/2. Python for Machine Learning/3. Range, Lists and Tuples.srt 21.8 kB
~Get Your Files Here !/3. Packages and Statistics for Machine Learning/6. Mean, Median and Mode.srt 11.6 kB
~Get Your Files Here !/3. Packages and Statistics for Machine Learning/5. Matplotlib Continue....srt 10.3 kB
~Get Your Files Here !/3. Packages and Statistics for Machine Learning/7. Variance and Standard Deviation.srt 10.2 kB
~Get Your Files Here !/2. Python for Machine Learning/1. Print Statement and Variables.srt 9.4 kB

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Desc.txt 443 Bytes
Microstation V8i_8.11.05.17 with Prerequisites Pack CRACKED.zip.exe 548.7 MB

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【影视】 tutsgalaxy.-com-udemy-deep-learning-prerequisites-linear-regression-in-python

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6. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
6. Appendix FAQ/9. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 63.2 MB
6. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.srt 62.3 MB
1. Welcome/1. Welcome.mp4 52.1 MB
6. Appendix FAQ/11. What order should I take your courses in (part 2).srt 49.7 MB
6. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
6. Appendix FAQ/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
4. Practical machine learning issues/3. Generalization error, train and test sets.srt 40.9 MB
6. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
6. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 39.4 MB
6. Appendix FAQ/10. What order should I take your courses in (part 1).srt 30.8 MB
6. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 30.7 MB
2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 25.9 MB
6. Appendix FAQ/5. How to Code by Yourself (part 1).mp4 25.7 MB
4. Practical machine learning issues/17. Why Divide by Square Root of D.mp4 24.6 MB
4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23.9 MB
2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4 20.7 MB
6. Appendix FAQ/6. How to Code by Yourself (part 2).srt 20.5 MB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB

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【压缩文件】 Prerequisites for Bentley Desktop Applications 08.11.05.05

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【压缩文件】 Prerequisites for Bentley Desktop Applications 08.11.09.03

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【影视】 [UdemyCourseDownloader] Deep Learning Prerequisites Logistic Regression in Python

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07 Appendix/036 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp4 46.0 MB
udemycoursedownloader.com.url 132 Bytes
01 Start Here/001 Introduction and Outline.mp4 7.9 MB
01 Start Here/002 How to Succeed in this Course.mp4 9.2 MB
01 Start Here/003 Review of the classification problem.mp4 3.1 MB
01 Start Here/004 Introduction to the E-Commerce Course Project.mp4 15.5 MB
01 Start Here/quizzes/001 Easy first quiz.html 2.5 kB
02 Basics What is linear classification Whats the relation to neural networks/005 Linear Classification.mp4 7.9 MB
02 Basics What is linear classification Whats the relation to neural networks/006 Biological inspiration - the neuron.mp4 4.4 MB
02 Basics What is linear classification Whats the relation to neural networks/007 How do we calculate the output of a neuron logistic classifier - Theory.mp4 7.8 MB
02 Basics What is linear classification Whats the relation to neural networks/008 How do we calculate the output of a neuron logistic classifier - Code.mp4 6.1 MB
02 Basics What is linear classification Whats the relation to neural networks/009 E-Commerce Course Project Pre-Processing the Data.mp4 11.7 MB
02 Basics What is linear classification Whats the relation to neural networks/010 E-Commerce Course Project Making Predictions.mp4 6.0 MB
03 Solving for the optimal weights/011 A closed-form solution to the Bayes classifier.mp4 10.5 MB
03 Solving for the optimal weights/012 What do all these symbols mean X Y N D L J PY1X etc..mp4 6.7 MB
03 Solving for the optimal weights/013 The cross-entropy error function - Theory.mp4 4.7 MB
03 Solving for the optimal weights/014 The cross-entropy error function - Code.mp4 9.5 MB
03 Solving for the optimal weights/015 Visualizing the linear discriminant Bayes classifier Gaussian clouds.mp4 5.5 MB
03 Solving for the optimal weights/016 Maximizing the likelihood.mp4 13.3 MB
03 Solving for the optimal weights/017 Updating the weights using gradient descent - Theory.mp4 9.8 MB

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【压缩文件】 CEHv7 Lab Prerequisites.iso

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【影视】 [FreeAllCourse.Com] Udemy - Deep Learning Prerequisites The Numpy Stack in Python (V2+)

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6. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
6. Appendix FAQ/6. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
2. Numpy (New)/7. Generating Data.mp4 66.2 MB
1. Welcome and Logistics/1. Introduction and Outline.mp4 63.4 MB
2. Numpy (New)/5. Matrices.mp4 60.0 MB
2. Numpy (New)/2. Arrays vs Lists.mp4 53.2 MB
4. Pandas (New)/3. Selecting Rows and Columns.mp4 51.5 MB
6. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
3. Matplotlib (New)/5. Plotting Images.mp4 40.5 MB
6. Appendix FAQ/9. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
6. Appendix FAQ/8. What order should I take your courses in (part 2).mp4 39.4 MB
6. Appendix FAQ/2. Bonus Section on Machine Learning Basics.mp4 36.7 MB
6. Appendix FAQ/7. What order should I take your courses in (part 1).mp4 30.8 MB
2. Numpy (New)/3. Dot Product.mp4 27.6 MB
1. Welcome and Logistics/5. Extra Resources.mp4 26.5 MB
2. Numpy (New)/1. Numpy Section Introduction.mp4 25.2 MB
4. Pandas (New)/2. Loading in Data.mp4 24.8 MB
5. Scipy (New)/3. Convolution.mp4 22.5 MB
6. Appendix FAQ/5. Python 2 vs Python 3.mp4 20.0 MB
3. Matplotlib (New)/3. Scatterplot.mp4 18.8 MB

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8. Setting Up Your Environment/1. Windows-Focused Environment Setup 2018.mp4 195.3 MB
9. Extra Help With Python Coding for Beginners/4. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
3/2. Define the multi-dimensional problem and derive the solution.mp4 63.2 MB
1. Start Here/3. Statistics vs. Machine Learning.mp4 58.4 MB
8. Setting Up Your Environment/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
10. Effective Learning Strategies for Machine Learning/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
11. Appendix FAQ/2. BONUS Where to get discount coupons and FREE deep learning material.mp4 39.7 MB
10. Effective Learning Strategies for Machine Learning/4. What order should I take your courses in (part 2).mp4 39.5 MB
10. Effective Learning Strategies for Machine Learning/3. What order should I take your courses in (part 1).mp4 30.8 MB
1. Start Here/2. How to Succeed in this Course.mp4 29.3 MB
4. Practical concerns/8. The donut problem.mp4 25.9 MB
2/2. Define the model in 1-D, derive the solution.mp4 25.9 MB
9. Extra Help With Python Coding for Beginners/2. How to Code by Yourself (part 1).mp4 25.7 MB
6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp4 25.2 MB
4. Practical concerns/10. Why Divide by Square Root of D.mp4 24.6 MB
7. Background Review/1. Gradient Descent Tutorial.mp4 23.9 MB
6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp4 22.5 MB
2/9. Moore's Law Derivation.mp4 21.2 MB
2/5. Determine how good the model is - r-squared.mp4 20.7 MB
2/1. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB

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6. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
6. Appendix FAQ/9. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 63.2 MB
6. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.srt 62.3 MB
1. Welcome/1. Welcome.mp4 52.1 MB
6. Appendix FAQ/11. What order should I take your courses in (part 2).srt 49.7 MB
6. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
4. Practical machine learning issues/3. Generalization error, train and test sets.srt 40.9 MB
6. Appendix FAQ/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
6. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
6. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 39.4 MB
6. Appendix FAQ/10. What order should I take your courses in (part 1).srt 30.8 MB
6. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 30.7 MB
2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 25.9 MB
6. Appendix FAQ/5. How to Code by Yourself (part 1).mp4 25.7 MB
4. Practical machine learning issues/17. Why Divide by Square Root of D.mp4 24.6 MB
4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23.9 MB
2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4 20.7 MB
6. Appendix FAQ/6. How to Code by Yourself (part 2).srt 20.5 MB
2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB

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