D2L.ai: Interactive Deep Learning Book with Multi-Framework Code, Math, and Discussions

Book website | STAT 157 Course at UC Berkeley

This open-source book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained 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;
5. be complemented by a forum for interactive discussion of technical details and to answer questions.

Universities Using D2L

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

@book{zhang2023dive,
    title={Dive into Deep Learning},
    author={Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J.},
    publisher={Cambridge University Press},
    note={\url{https://D2L.ai}},
    year={2023}
}

Endorsements

Contributing (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.

Dear D2L contributors, please email your GitHub ID and name to d2lbook.en AT gmail DOT com so your name will appear on the acknowledgments. Thanks.

License Summary

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

Chinese version | Discuss and report issues | Code of conduct