d2l-ai/d2l-en
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
From the publisher
README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
[](https://github.com/d2l-ai/d2l-en/actions/workflows/ci.yml) Book website | STAT 157 Course at UC Berkeley 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 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 If you find this book useful, please star (★) this repository or cite this book using the following bibtex entry: 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…
Read the full README ↗ · Preview checked 2026-11-01T09:50:52.741Z
Inside the original README — Document outline
- D2L.ai: Interactive Deep Learning Book with Multi-Framework Code, Math, and Discussions
- Universities Using D2L
- Endorsements
- Contributing (Learn How)
- License Summary
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What this repository does
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Repository facts
- Owner
- d2l-ai
- Primary language
- Python
- Stars
- 29,978
- Forks
- 5,150
- Open issues + pull requests
- 200
- License
- Other
- Archived
- No
- Default branch
- master
- Created
- 2018-10-09T01:04:37.000Z
- Last push
- 2024-08-18T08:02:36.000Z
Topics and intended use
Owner-supplied topics: book, computer-vision, data-science, deep-learning, gaussian-processes, hyperparameter-optimization, jax, kaggle, keras, machine-learning, mxnet, natural-language-processing, notebook, python, pytorch, recommender-system, reinforcement-learning, tensorflow
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Source and freshness
Source: GitHub. Metadata observed 2026-11-01T12:19:42.808Z. Daily imports are snapshots, not real-time monitoring.
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