AI实战圣经《Machine Learning Yearning》第1-52章中英文版pdf分享
《Machine Learning Yearning》是机器学习泰斗Andrew NG花了近2年时间，根据自己多年实践经验整理出来的一本机器学习、深度学习实践经验宝典。本书的重点不在于教授传统的机器学习算法理论基础，而在于教你如何在实践中使机器学习算法的实战经验。如果你渴望成为AI的技术领导者，并想要学习如何为团队设定一个方向，本书将有所帮助。
本书官方网址：http://www.mlyearning.org/
台主花了几天时间对本书1-52节的中英文内容进行了整理，内容整理自网络。文末附本书中文和英文pdf下载地址，仅供学习分享。
本书主要总结了50多个吴恩达多年在AI领域的工程要领，把每一个要领都浓缩到 1-2 页的阅读量，非常精炼。目前，前52个要领已经分享出来了，被分为9个主题。
前9个主题列表
第一章：绪论 「Introduction」
第二章：配置开发集和训练集 「Setting up development and test sets」
第三章：基本误差分析 「Basic Error Analysis」
第四章：偏差和方差 「Bias and Variance」
第五章：学习曲线 「Learning curves」
第六章：比较人类水平表现 「Comparing to human-level performance」
第七章：不同分布下的训练和测试 「Training and testing on different distributions」
第八章：调试推理算法 「Debugging inference algorithms」
第九章：端到端的深度学习 「End-to-end deep learning」
前52个要领列表
（英文列表，保证原汁原味）
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
书籍下载地址
英文版下载地址：
扫描下方二维码，关注公众号深度学习与NLP，回复“ngmle”，获取下载地址
中文版下载地址：
扫描下方二维码，关注公众号深度学习与NLP，回复“ngmle”，获取下载地址
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