《Machine Learning Yearning》是機器學習泰斗Andrew NG花了近2年時間，根據自己多年實踐經驗整理出來的一本機器學習、深度學習實踐經驗寶典。本書的重點不在於教授傳統的機器學習算法理論基礎，而在於教你如何在實踐中使機器學習算法的實戰經驗。如果你渴望成為AI的技術領導者，並想要學習如何為團隊設定一個方向，本書將有所幫助。
本書官方網址：http://www.mlyearning.org/
臺主花了幾天時間對本書1-52節的中英文內容進行了整理，內容整理自網絡。文末附本書中文和英文pdf下載地址，僅供學習分享。
本書主要總結了50多個吳恩達多年在AI領域的工程要領，把每一個要領都濃縮到 1-2 頁的閱讀量，非常精煉。目前，前52個要領已經分享出來了，被分為9個主題。
前9個主題列表
第一章：緒論 「Introduction」
第二章：配置開發集和訓練集 「Setting up development and test sets」
第三章：基本誤差分析 「Basic Error Analysis」
第四章：偏差和方差 「Bias and Variance」
第五章：學習曲線 「Learning curves」
第六章：比較人類水平表現 「Comparing to human-level performance」
第七章：不同分佈下的訓練和測試 「Training and testing on different distributions」
第八章：調試推理算法 「Debugging inference algorithms」
第九章：端到端的深度學習 「End-to-end deep learning」
前52個要領列表
（英文列表，保證原汁原味）
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 Evaluating multiple ideas in parallel during error analysis
16 Cleaning up mislabeled dev and test set examples
17 If you have a large dev set, split it into two subsets, only one of which you look at
18 How big should the Eyeball and Blackbox dev sets be?
19 Takeaways: Basic error analysis
20 Bias and Variance: The two big sources of error
21 Examples of Bias and Variance
22 Comparing to the optimal error rate
23 Addressing Bias and Variance
24 Bias vs. Variance tradeoff
25 Techniques for reducing avoidable bias
Page 3 Machine Learning Yearning-Draft Andrew Ng26 Techniques for reducing Variance
27 Error analysis on the training set
28 Diagnosing bias and variance: Learning curves
29 Plotting training error
30 Interpreting learning curves: High bias
31 Interpreting learning curves: Other cases
32 Plotting learning curves
33 Why we compare to human-level performance
34 How to define human-level performance
35 Surpassing human-level performance
36 Why train and test on different distributions
37 Whether to use all your data
38 Whether to include inconsistent data
39 Weighting data
40 Generalizing from the training set to the dev set
41 Addressing Bias, and Variance, and Data Mismatch
42 Addressing data mismatch
43 Artificial data synthesis
44 The Optimization Verification test
45 General form of Optimization Verification test
46 Reinforcement learning example
47 The rise of end-to-end learning
48 More end-to-end learning examples
49 Pros and cons of end-to-end learning
50 Learned sub-components
51 Directly learning rich outputs
52 Error Analysis by Parts
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