Dive into Deep Learning (D2L Book)

Book website | STAT 157 Course at UC Berkeley, Spring 2019

The best way to understand deep learning is learning by doing.

This open-source book represents our attempt to make deep learning approachable, teaching you both the concepts, the context, and the code.

Our goal is to offer a resource that could (1) be freely available for everyone; (2) offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist; (3) include runnable code, showing readers how to solve problems in practice; (4) allow for rapid updates, both by us and also by the community at large; and (5) be complemented by a forum for interactive discussion of technical details and to answer questions.

Universities that use D2L as a textbook or a reference book

If you find this book useful, please star (★) this repository or cite this book using the following bibtex entry:

@book{zhang2020dive,
    title={Dive into Deep Learning},
    author={Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola},
    note={\url{https://d2l.ai}},
    year={2020}
}

Contribute (learn how)

This open source book has benefited from pedagogical suggestions, typo corrections, and other improvements from community contributors. Your help is valuable for making the book better for everyone. We will acknowledge each D2L contributor in the book and send a free book (hard copy) to the first 100 contributors when it is published.

Dear D2L contributors, please email your GitHub ID, name, and mailing address to [email protected]. Thanks.

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License Summary

This open source book is made available under the Creative Commons Attribution-ShareAlike 4.0 International License. See the LICENSE file.

The sample and reference code within this open source book is made available under a modified MIT license. See the LICENSE-SAMPLECODE file.

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