Machine Learning Yearning 机器学习秘籍

Chinese Translation of Machine Learning Yearning by Andrew Ng

AcceptedDoge/machine-learning-yearning-cn 已获得Andrew Ng授权中文版翻译，有兴趣者可点击链接参与翻译。此仓库仅供我个人学习使用。

How do you organize an AI project?

Why Machine Learning Strategy
How to use this book to help your team
Prerequisites and Notation
Scale drives machine learning progress
Your development and test sets
Your dev and test sets should come from the same distribution
How large do the dev/test sets need to be?
Establish a single-number evaluation metric for your team to optimize
Optimizing and satisficing metrics
Having a dev set and metric speeds up iterations
When to change dev/test sets and metrics
Takeaways: Setting up development and test sets
Build your first system quickly, then iterate
Error analysis: Look at dev set examples to evaluate ideas
Evaluating multiple ideas in parallel during error analysis
Cleaning up mislabeled dev and test set examples
If you have a large dev set, split it into two subsets, only one of which you look at
How big should the Eyeball and Blackbox dev sets be?
Takeaways: Basic error analysis
Bias and Variance: The two big sources of error
Examples of Bias and Variance
Comparing to the optimal error rate
Addressing Bias and Variance
Bias vs. Variance tradeoff
Techniques for reducing avoidable bias
Error analysis on the training set
Techniques for reducing variance
Diagnosing bias and variance: Learning curves
Plotting training error
Interpreting learning curves: High bias
Interpreting learning curves: Other cases
Plotting learning curves
Why we compare to human-level performance
How to define human-level performance
Surpassing human-level performance
Why train and test on different distributions
Whether to use all your data
Whether to include inconsistent data
Weighting data
Generalizing from the training set to the dev set
Identifying Bias, and Variance, and Data Mismatch
Addressing data mismatch
Artificial data synthesis Update to Here
The Optimization Verification test
General form of Optimization Verification test
Reinforcement learning example
The rise of end-to-end learning
More end-to-end learning examples
Pros and cons of end-to-end learning
Learned sub-components
Directly learning rich outputs
Error Analysis by Parts
Beyond supervised learning: What’s next?
Building a superhero team - Get your teammates to read this
Big picture
Credits

TO IMPROVE:

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英文原文

当前更新了所有章节，共58章，详见 draft-copy-of-machine-learning-yearning 目录：

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