Dive into Deep Learning

Dive into Deep Learning

Interactive deep learning book with code, math, and discussions

Implemented with NumPy/MXNet, PyTorch, and TensorFlow

Adopted at 200 universities from 50 countries


  • [Jul 2021] We have improved the content for v1.0, 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.


Aston Zhang

Amazon Senior Scientist

Zack C. Lipton

Amazon Scientist
CMU Assistant Professor

Mu Li

Amazon Senior Principal Scientist

Alex J. Smola

Amazon VP/Distinguished Scientist

Vol.2 Chapter Authors

Shuai Zhang

ETH Zürich Postdoctoral Researcher
Recommender Systems

Yi Tay

Google Scientist
Recommender Systems

Framework Adaptation Authors

Anirudh Dagar

IIT Roorkee Student
PyTorch Adaptation

Yuan Tang

Ant Group Senior Engineer
TensorFlow Adaptation

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

Contribute to the book.

Star Fork

Each section is an executable Jupyter notebook

You can modify the code and tune hyperparameters to get instant feedback to accumulate practical experiences in deep learning.

Mathematics + Figures + Code

We offer an interactive learning experience with mathematics, figures, code, text, and discussions, where concepts and techniques are illustrated and implemented with experiments on real data sets.

Active community support

You can discuss and learn with thousands of peers in the community through the link provided in each section.

D2L as a textbook or a reference book

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If you use (or will use) D2L to teach and would like to receive a free hardcopy, please contact us.

BibTeX entry for citing the book

    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},

Table of contents