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：

    公众号回复“ngmle”，获取下载地址

    中文版下载地址：

    分享朋友圈，获取5个赞，截图并公众号回复获取下载地址。

  （整理不易，人数较多，回复可能有延迟，谢谢理解）

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