This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to:
- Prioritize the most promising directions for an AI project
- Diagnose errors in a machine learning system
- Build ML in complex settings, such as mismatched training/ test sets
- Set up an ML project to compare to and/or surpass human- level performance
- Know when and how to apply end-to-end learning, transfer learning, and multi-task learning.
Table of contents
- 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
- When you should train and test on different distributions
- How to decide whether to use all your data
- How to decide whether to include inconsistent data
- Weighting data
- Generalizing from the training set to the dev set
- Identifying Bias, Variance, and Data Mismatch Errors
- Addressing data mismatch
- Artificial data synthesis
- 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
- Choosing pipeline components: Data availability
- Choosing pipeline components: Task simplicity
- Directly learning rich outputs
- Error analysis by parts
- Attributing error to one part
- General case of error attribution
- Error analysis by parts and comparison to human-level performance
- Spotting a flawed ML pipeline
- Building a superhero team - Get your teammates to read this
| Pages : | 118 |
| Size : | 4.1 MB |
| Downloads: | 140 |
| Created: | 2022-02-03 |
| License: | CC BY |
| Author(s): | Andrew Ng |
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