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MLY S00 Table of Contents

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Machine Learning Yearning S00 Table of Contents

Table of Contents (draft)

  1. Why Machine Learning Strategy

  2. How to use this book to help your team

  3. Prerequisites and Notation

  4. Scale drives machine learning progress

  5. Your development and test sets

  6. Your dev and test sets should come from the same distribution

  7. How large do the dev/test sets need to be?

  8. Establish a single-number evaluation metric for your team to optimize

  9. Optimizing and satisficing metrics

  10. Having a dev set and metric speeds up iterations

  11. When to change dev/test sets and metrics

  12. Takeaways: Setting up development and test sets

  13. Build your first system quickly, then iterate

  14. Error analysis: Look at dev set examples to evaluate ideas

  15. Evaluate multiple ideas in parallel during error analysis

  16. If you have a large dev set, split it into two subsets, only one of which you look at

  17. How big should the Eyeball and Blackbox dev sets be?

  18. Takeaways: Basic error analysis

  19. Bias and Variance: The two big sources of error

  20. Examples of Bias and Variance

  21. Comparing to the optimal error rate

  22. Addressing Bias and Variance

  23. Bias vs

  24. Techniques for reducing avoidable bias

  25. Techniques for reducing Variance

  26. Error analysis on the training set

  27. Diagnosing bias and variance: Learning curves

  28. Plotting training error

  29. Interpreting learning curves: High bias

  30. Interpreting learning curves: Other cases

  31. Plotting learning curves

  32. Why we compare to human-level performance

  33. How to define human-level performance

  34. Surpassing human-level performance

  35. Why train and test on different distributions

  36. Whether to use all your data

  37. Whether to include inconsistent data

  38. Weighting data

  39. Generalizing from the training set to the dev set

  40. Addressing Bias and Variance

  41. Addressing data mismatch

  42. Artificial data synthesis

  43. The Optimization Verification test

  44. General form of Optimization Verification test

  45. Reinforcement learning example

  46. The rise of end-to-end learning

  47. More end-to-end learning examples

  48. Pros and cons of end-to-end learning

  49. Learned sub-components

  50. Directly learning rich outputs

  51. Error Analysis by Parts

  52. Beyond supervised learning: What’s next?

  53. Building a superhero team - Get your teammates to read this

  54. Big picture

  55. Credits

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