Machine Learning Yearning.
Picture Of The Book:
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| Machine Learning Yearning. |
About The Book:
This book is written for those candidates who wish to learn the technical implementation strategy of machine learning. This book focuses primarily on all concepts of "machine learning" with the real world, at the enterprise level, and best practices for the implementation and use of production services.
Information About The Book:
Title: Machine Learning Yearning: Technical Strategy for AI Engineers, In the Era of Deep Learning.
Language: English.
Size: 4.14 Mb.
Pages: 118.
Format: Pdf.
Year: 2018.
Edition: 1.
Author: Andrew NG.
Contents Of The Book:
Chapter 1: Why Machine Learning Strategy.
Chapter 2: How to use this book to help your team.
Chapter 3: Prerequisites and Notation.
Chapter 4: Scale drives machine learning progress.
Chapter 5: Your development and test sets.
Chapter 6: Your dev and test sets should come from the same distribution.
Chapter 7: How large do the dev/test sets need to be?
Chapter 8: Establish a single-number evaluation metric for your team to optimize.
Chapter 9: Optimizing and satisficing metrics.
Chapter 10: Having a dev set and metric speeds up iterations.
Chapter 11: When to change dev/test sets and metrics.
Chapter 12: Takeaways: Setting up development and test sets.
Chapter 13: Build your first system quickly, then iterate.
Chapter 14: Error analysis: Look at dev set examples to evaluate ideas.
Chapter 15: Evaluating multiple ideas in parallel during error analysis.
Chapter 16: Cleaning up mislabeled dev and test set examples.
Chapter 17: If you have a large dev set, split it into two subsets, only one of which you look at.
Chapter 18: How big should the Eyeball and Blackbox dev sets be?
Chapter 19: Takeaways: Basic error analysis.
Chapter 20: Bias and Variance: The two big sources of error.
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