Machine Learning ten years ago. Ten years ago, Ten years ago, Ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago. \
Back in early April, Ng introduced the book: “The point of this book is not to teach you machine learning algorithms, but to teach you how to use them for your own benefit.” Like some technical AI classes give you a hammer, but this book teaches you how to use the hammer. If you aspire to be a technology leader in artificial intelligence and want to learn how to set the direction for your team, this book will help.
After reading this book, you will have learned:
Identify the most promising directions for ai projects;
Diagnosing errors in machine learning systems;
Build ML in complex Settings such as mismatched training/test sets;
Set up an ML project to compare with human performance;
Learn when and how to apply end-to-end learning, transfer learning, and multitasking learning.
Screenshots:
Full contents: \
1 Why does machine learning need strategy? \
How can you use this book to help your team
3. Knowledge and symbol description
4 scale drives machine learning
5 Definition of development set and test set
Development and test sets should follow the same distribution
How big should the development and test sets be? ?
8. Use single value evaluation indicators for optimization
9 optimization indicators and satisfaction indicators
Accelerate iteration through development sets and metrics
11 When to modify the development set, test set, and metrics
Summary: Build a development set and a test set
Quickly build and iterate on your first system
Error analysis: Evaluate ideas against development set samples
Evaluate multiple ideas in parallel during error analysis
16 Clean mislabeled development set and test set samples
Divide the large development set into two subsets, focusing on one
How big is this setting?
Summary: Basic error analysis
Bias and variance: Two major sources of error
21 Bias and variance examples
22 compared with the optimal error rate
23 Deal with bias and variance
Tradeoff between bias and variance
Reduce techniques that can avoid deviations
26 Error Analysis of training set \
Techniques for reducing variance
Diagnostic bias and variance: the learning curve
29 Draw the training error curve
Reading the learning curve: High bias
Reading the learning curve: Other cases
32 Draw a learning curve
33 Why is it compared to human performance
How to define human performance level
35 Exceeds human performance
When to train and test on different distributions
How do you decide whether to use all your data
38 How do I determine whether to add inconsistent data
39 Add weights to data
40 From training set generalization to development set
Identify bias, variance and data mismatch errors
42 Resolve the data mismatch problem
43 Synthetic data
44 Optimize validation tests
Optimize the general form of validation tests
46 Examples of reinforcement learning
The rise of end-to-end learning
More examples of end-to-end learning
The pros and cons of end-to-end learning
Pipeline component selection: data availability
Pipelining component selection: task simplicity
Page 4 Machine Learning Yearning-Draft Andrew Ng
52 Direct learning for richer output
53 Perform error analysis based on components
54 Error is attributed to a component
The general case of error attribution
56 Component error analysis compared to human level
57 Discovering a flawed machine learning pipeline
Build superhero teams – Get your teammates to read this book
Deeplearning. ai has published a translated version of the book.
Link: pan.baidu.com/s/1OmlO8fAZ…
Extraction code: ZR03
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