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Download Book :  Machine Learning Yearning By Andrew Ng PDF

Informations about the book:

Title: Machine Learning Yearning

Author:  Andrew Ng  

Size4.1 MB




Book 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
26 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 Identifying Bias, Variance, and Data Mismatch Errors
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 Choosing pipeline components: Data availability
51 Choosing pipeline components: Task simplicity
52 Directly learning rich outputs
53 Error analysis by parts
54 Attributing error to one part
55 General case of error attribution
56 Error analysis by parts and comparison to human-level performance
57 Spotting a flawed ML pipeline
58 Building a superhero team - Get your teammates to read this


After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project. 
But your teammates might not understand why you’re recommending a particular direction. Perhaps you want your team to define a single-number evaluation metric, but they aren’t convinced. How do you persuade them? 
That’s why I made the chapters short: So that you can print them out and get your teammates to read just the 1-2 pages you need them to know.
A few changes in prioritization can have a huge effect on your team’s productivity. By helping your team with a few such changes, I hope that you can become the superhero of your team! 

5-Best Arduino books :

1-Machine Learning with PySpark: With Natural Language Processing and Recommender Systems

2-Machine Learning: An Essential Guide to Machine Learning for Beginners

3-Applied Machine Learning with Python

4-Industrial applications of machine learning

5-Machine Learning For Dummies


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