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BT种子基本信息

  • 种子哈希:d2774faa2156f4110893ec559be834d70d587ef8
  • 文档大小:15.9 GB
  • 文档个数:391个文档
  • 下载次数:822
  • 下载速度:极快
  • 收录时间:2021-04-03
  • 最近下载:2025-05-15
  • DMCA/屏蔽:DMCA/屏蔽

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Complete 2020 Data Science & Machine Learning Bootcamp的二维码

文档列表

  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/4. Introduction to Optimisation and the Gradient Descent Algorithm/8. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 305.5 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/4. Introduction to Optimisation and the Gradient Descent Algorithm/6. [Python] - Loops and the Gradient Descent Algorithm.mp4 301.4 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4 264.1 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/5. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 256.0 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/4. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4 248.1 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/3. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4 243.4 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/4. Introduction to Optimisation and the Gradient Descent Algorithm/9. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 229.7 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.mp4 228.9 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/5. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 224.8 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4 224.1 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/8. Test and Evaluate a Naive Bayes Classifier Part 3/6. Visualising the Decision Boundary.mp4 215.3 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/8. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4 204.6 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/4. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4 202.9 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/3. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4 179.8 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/3. Python Programming for Data Science and Machine Learning/19. Working with Python Objects to Analyse Data.mp4 178.2 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/5. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4 176.8 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/3. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4 164.4 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4 162.9 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/3. Python Programming for Data Science and Machine Learning/9. [Python & Pandas] - Dataframes and Series.mp4 160.7 MB
  • mp4Complete 2020 Data Science & Machine Learning Bootcamp/5. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 160.4 MB
  • ==查看完整文档列表==
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