d2l-en README

The project's README from github.com/d2l-ai/d2l-en. As one markdown file: README.md.

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

Continuous Integration

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

  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

"In less than a decade, the AI revolution has swept from research labs to broad industries to every corner of our daily life. Dive into Deep Learning is an excellent text on deep learning and deserves attention from anyone who wants to learn why deep learning has ignited the AI revolution: the most powerful technology force of our time."

— Jensen Huang, Founder and CEO, NVIDIA

"This is a timely, fascinating book, providing with not only a comprehensive overview of deep learning principles but also detailed algorithms with hands-on programming code, and moreover, a state-of-the-art introduction to deep learning in computer vision and natural language processing. Dive into this book if you want to dive into deep learning!"

— Jiawei Han, Michael Aiken Chair Professor, University of Illinois at Urbana-Champaign

"This is a highly welcome addition to the machine learning literature, with a focus on hands-on experience implemented via the integration of Jupyter notebooks. Students of deep learning should find this invaluable to become proficient in this field."

— Bernhard Schölkopf, Director, Max Planck Institute for Intelligent Systems

"Dive into Deep Learning strikes an excellent balance between hands-on learning and in-depth explanation. I've used it in my deep learning course and recommend it to anyone who wants to develop a thorough and practical understanding of deep learning."

— Colin Raffel, Assistant Professor, University of North Carolina, Chapel Hill

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

d2l-en (d2l-ai/d2l-en)

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D2l-en
这是什么东西？一本交互式的开源深度学习书籍，把概念、背景知识和可运行的代码放在一起讲，并提供了多个框架的示例。它的理念是：学深度学习最好的方式就是动手实践。
它是只用来读的书，还是可以真正跑起来的代码？两者都是。整本书都是以 Jupyter 笔记本的形式编写的，把文字、图表、数学公式和可以独立运行的代码整合在一起，所以阅读和动手实验可以在同一个地方完成。
我该怎么开始使用它？可以在 d2l.ai 上免费在线阅读，也可以从这个仓库获取笔记本在本地运行；遇到问题还可以在论坛里提问和讨论。
这份代码支持哪些深度学习框架？代码支持多个框架：根据仓库的主题标签，它覆盖了 MXNet、PyTorch、TensorFlow、Keras 和 JAX，你可以用自己偏好的框架跟着学。
如果我完全不懂深度学习，适合看这本书吗？这本书的写作方式对新手很友好，同时也具备足够的技术深度，能带你走上成为一名应用机器学习科学家的道路。
这本书真的有大学在用吗？有——全球 70 个国家的 500 所大学都在使用它，其中包括斯坦福、MIT、哈佛和剑桥，UC Berkeley 还用它来开设 STAT 157 课程。
都有谁推荐过这本书？README 中收录了 Jensen Huang（NVIDIA CEO）、Jiawei Han（伊利诺伊大学）、Bernhard Schölkopf（马克斯·普朗克智能系统研究所）和 Colin Raffel（北卡罗来纳大学教堂山分校）的推荐。
我能在学术作品中引用它吗？可以——README 希望读者给仓库点个星，或者用它提供的 bibtex 条目来引用这本书。
What are similar projects?DeepLearning, Ai-Learn, 100-Days-Of-ML-Code, deep-learning-with-python-notebooks, CV, d2l-zh, ailearning, pytorch-handbook
Which topics is it in?recommender-system, hyperparameter-optimization, mxnet, kaggle, notebook, jax, keras, neural-networks, jupyter-notebook, tensorflow, natural-language-processing, curriculum
Creator
d2l-ai
Size
310.5 MB
Stars
29.9K
Updated
CommitMar 16Releasev1.0.3Aug 18
Age
8y
Commits · 90 Days