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Dive into Deep Learning, Berkeley STAT 157 (Spring 2019) textbook. With code, math, and discussions.

d2l.ai

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Name		Name	Last commit message	Last commit date
Latest commit History 316 Commits
build		build
chapter_appendix		chapter_appendix
chapter_computational-performance		chapter_computational-performance
chapter_computer-vision		chapter_computer-vision
chapter_convolutional-neural-networks		chapter_convolutional-neural-networks
chapter_crashcourse		chapter_crashcourse
chapter_deep-learning-basics		chapter_deep-learning-basics
chapter_deep-learning-computation		chapter_deep-learning-computation
chapter_introduction		chapter_introduction
chapter_natural-language-processing		chapter_natural-language-processing
chapter_optimization		chapter_optimization
chapter_prerequisite		chapter_prerequisite
chapter_recurrent-neural-networks		chapter_recurrent-neural-networks
contrib/chapter_crashcourse		contrib/chapter_crashcourse
data		data
graffle		graffle
img		img
.gitignore		.gitignore
.gitmodules		.gitmodules
Jenkinsfile		Jenkinsfile
Makefile		Makefile
README.md		README.md
STYLE_GUIDE.md		STYLE_GUIDE.md
environment.yml		environment.yml

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Dive into Deep Learning (D2L Book)

Become a D2L contributor, and you will be acknowledged in the book

The contents of the book are under revision. We expect that the quality will be constantly improved.

Feel free to contribute to this book (learn how to contribute). All the contributors (Github ID and name, if any) will be acknowledged in the book. We will also negotiate with the press to see if each contributor can receive a free book when published. Once you become a D2L contributor, please email your Github ID and name to