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:
- Write Math Equations with MathJax, HTML.
- 最新版章节更新与此不一致，等有空再看了。
当前更新了所有章节，共58章，详见 draft-copy-of-machine-learning-yearning 目录：