Dive into Deep Learning¶

Announcements

[Dec 2021] We added a new option to run this book for free: check out SageMaker Studio Lab.
[Jul 2021] We have improved the content and added TensorFlow implementations up to Chapter 11. To keep track of the latest updates, just follow D2L's open-source project.
[Jan 2021] Check out the brand-new Chapter: Attention Mechanisms. We have also added PyTorch implementations.
[Apr 2020] We have revamped Chapter: NLP pretraining and Chapter: NLP applications, and added sections of BERT and natural language inference.
[Jul 2019] The Chinese version is the No. 1 best seller of new books in "Computers and Internet" at the largest Chinese online bookstore.
[May 2019] Slides, Jupyter notebooks, assignments, and videos of the Berkeley course can be found at the syllabus page.

Authors

Zack C. Lipton

Amazon Scientist
CMU Assistant Professor

Vol.2 Chapter Authors

Brent Werness

Amazon Senior Scientist
Mathematics for Deep Learning

Rachel Hu

Amazon Scientist
Mathematics for Deep Learning

Shuai Zhang

ETH Zürich Postdoctoral Researcher
Recommender Systems

Framework Adaptation Authors

Anirudh Dagar

IIT Roorkee Student
PyTorch Adaptation

Yuan Tang

Akuity Founding Engineer
TensorFlow Adaptation

Portuguese Translators

We thank all the community contributors
for making this open source book better for everyone.

Each section is an executable Jupyter notebook

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BibTeX entry for citing the book

          
            @article{zhang2021dive,
    title={Dive into Deep Learning},
    author={Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J.},
    journal={arXiv preprint arXiv:2106.11342},
    year={2021}
}
          
        

Dive into Deep Learning¶