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  • Урок 1. 00:31:10
    1. Telling the Computer What We Want
  • Урок 2. 00:17:07
    2. Starting with Python Notebooks and Colab
  • Урок 3. 00:31:10
    3. Decision Trees for Logical Rules
  • Урок 4. 00:30:27
    4. Neural Networks for Perceptual Rules
  • Урок 5. 00:28:32
    5. Opening the Black Box of a Neural Network
  • Урок 6. 00:28:43
    6. Bayesian Models for Probability Prediction
  • Урок 7. 00:27:46
    7. Genetic Algorithms for Evolved Rules
  • Урок 8. 00:29:03
    8. Nearest Neighbors for Using Similarity
  • Урок 9. 00:27:36
    9. The Fundamental Pitfall of Overfitting
  • Урок 10. 00:27:51
    10. Pitfalls in Applying Machine Learning
  • Урок 11. 00:27:14
    11. Clustering and Semi Supervised Learning
  • Урок 12. 00:30:13
    12. Recommendations with Three Types of Learning
  • Урок 13. 00:29:34
    13. Games with Reinforcement Learning
  • Урок 14. 00:27:00
    14. Deep Learning for Computer Vision
  • Урок 15. 00:29:56
    15. Getting a Deep Learner Back on Track
  • Урок 16. 00:30:04
    16. Text Categorization with Words as Vectors
  • Урок 17. 00:29:29
    17. Deep Networks That Output Language
  • Урок 18. 00:29:27
    18. Making Stylistic Images with Deep Networks
  • Урок 19. 00:30:26
    19. Making Photorealistic Images with GANs
  • Урок 20. 00:30:19
    20. Deep Learning for Speech Recognition
  • Урок 21. 00:29:19
    21. Inverse Reinforcement Learning from People
  • Урок 22. 00:29:54
    22. Causal Inference Comes to Machine Learning
  • Урок 23. 00:29:59
    23. The Unexpected Power of Over Parameterization
  • Урок 24. 00:30:44
    24. Protecting Privacy within Machine Learning
  • Урок 25. 00:33:32
    25. Mastering the Machine Learning Process