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machine-learning-yearning-cn

吴恩达《Machine Learning Yearning》的英文版完结：[第1~第58章](Machine Learning Yearning 1-58（by Andrew NG）.pdf)

官网：

• http://www.mlyearning.org/
• https://www.deeplearning.ai/machine-learning-yearning/
• https://d2wvfoqc9gyqzf.cloudfront.net/content/uploads/2018/09/Ng-MLY01-13.pdf

原作者：Andrew NG

申明：本文旨在传播知识，并无商业行为之意

TODO

• 中文版因为版权问题，暂时无法更新

目录

Chapter 1. Why Machine Learning Strategy

Chapter 2. How to use this book to help your team

Chapter 3. Prerequisites and Notation

Chapter 4. Scale drives machine learning progress

Chapter 5. Your development and test sets

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

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

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

Chapter 9. Optimizingandsatisficingmetrics

Chapter 10. Having a dev set and metric speeds up iterations

Chapter 11. When to change dev/test sets and metrics

Chapter 12. Takeaways: Setting up development and test sets

Chapter 13. Build your first system quickly, then iterate

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

Chapter 15. Evaluate multiple ideas in parallel during error analysis

Chapter 16. Cleaning up mislabeled dev and test set examples

Chapter 17. If you have a large dev set, split it into two subsets, only one of which you look at

Chapter 18. How big should the Eyeball and Blackbox dev sets be?

Chapter 19. Takeaways: Basic error analysis

Chapter 20. Bias and Variance: The two big sources of error

Chapter 21. Examples of Bias and Variance

Chapter 22. Comparing to the optimal error rate

Chapter 23. Addressing Bias and Variance

Chapter 24. Bias vs. Variance tradeoff

Chapter 25. Techniques for reducing avoidable bias

Chapter 26. Techniques for reducing Variance

Chapter 27. Error analysis on the training set

Chapter 28. Diagnosing bias and variance: Learning curves

Chapter 29. Plotting training error

Chapter 30. Interpreting learning curves: High bias

Chapter 31. Interpreting learning curves: Other cases

Chapter 32. Plotting learning curves

Chapter 33. Why we compare to human-level performance

Chapter 34. How to define human-level performance

Chapter 35. Surpassing human-level performance

Chapter 36. Why train and test on different distributions

Chapter 37. Whether to use all your data

Chapter 38. Whether to include inconsistent data

Chapter 39. Weighting data

Chapter 40. Generalizing from the training set to the dev set

Chapete 41. Identifying Bias, Variance, and Data Mismatch Errors

Chapter 42. Addressing data mismatch

Chapter 43. Artificial data synthesis

Chapter 44. The Optimization Verification test

Chapter 45. General form of Optimization Verification test

Chapter 46. Reinforcement learning example

Chapter 47. The rise of end-to-end learning

Chapter 48. More end-to-end learning examples

Chapter 49. Pros and cons of end-to-end learning

Chapter 50. Choosing pipeline components: Data availability

Chapter 51. Choosing pipeline components: Task simplicity

Chapter 52. Directly learning rich outputs

Chapter 53. Error Analysis by Parts

Chapter 54. Attributing error to one part

Chapter 55: General case of error attribution

Chapter 56. Error analysis by parts and comparison to human-level performance

Chapter 57. Spotting a flawed ML pipeline

Chapter 58. Building a superhero team - Get your teammates to read this