吴恩达新书-Machine Learning Yearning 中英文版(全)

介绍一本吴恩达新书,说新也不新,出来也有好大半年了,也许你早就看过,如果没有可以看看。

这是一本面向实战类的书,讲述的是吴恩达自己关于项目类的比较高认识的总结,吴恩达称:这本书的主题是“如何构建机器学习项目”。

“这本书的重点不在于教授具体的机器学习算法,而在于如何使机器学习算法真正发挥作用。有一些技术类的人工智能课程会给你一个锤子;而这本书会教你如何使用这个锤子。如果你渴望成为人工智能领域的技术领导者,并想了解如何为你的团队设定方向,那么这本书将会有所帮助。”

全书52个章节,每个章节不长,也就一页两页的样子,所以更偏向总结类的,看着也快,可以看看大师的总结。

不多说,上前几节目录:

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」

找到一个在线网站翻译的,手机或者电脑在线看非常棒:

https://xiaqunfeng.gitbooks.io/machine-learning-yearning/content/chapter21.html

想下载中英文的pdf的扫一扫关注下面公号“机器学习与大数据挖掘”,后台回复关键字【机器学习思维】免费获得链接。
在这里插入图片描述

Table of Contents 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 Error analysis on the training set 27 Techniques for reducing variance 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 When you should train and test on different distributions 37 How to decide whether to use all your data 38 How to decide whether to include inconsistent data 39 Weighting data 40 Generalizing from the training set to the dev set 41 Addressing Bias and Variance 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 Page 4 Machine Learning Yearning-Draft Andrew Ng52 Error Analysis by Parts 53 Beyond supervised learning: What’s next? 54 Building a superhero team - Get your teammates to read this 55 Big picture 56 Credits
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