Dive into Deep Learning

Dive into Deep Learning

With New API

Switch to Classic API

Interactive deep learning book with code, math, and discussions

Implemented with PyTorch, NumPy/MXNet, and TensorFlow

Adopted at 300 universities from 55 countries

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Authors

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

Akuity Founding Engineer
TensorFlow Adaptation

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

Contribute to the book.

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

Abasyn University, Islamabad Campus
Alexandria University
Amirkabir University of Technology
Amity University
Anna University
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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


@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}
}

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