Dive into Deep Learning

Dive into Deep Learning

Interactive deep learning book with code, math, and discussions

Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow

Adopted at 400 universities from 60 countries

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Authors

Aston Zhang

Amazon Senior Scientist

Zack C. Lipton

CMU Assistant Professor
Amazon Visiting Academic

Mu Li

Amazon Senior Principal Scientist

Alex J. Smola

Amazon VP/Distinguished Scientist

Vol.2 Chapter Authors

Pratik Chaudhari

UPenn Assistant Professor
Amazon Visiting Academic
Reinforcement Learning

Rasool Fakoor

Amazon Senior Scientist
Reinforcement Learning

Andrew Gordon Wilson

NYU Associate Professor
Amazon Visiting Academic
Gaussian Processes

Matthias Seeger

Amazon Principal Scientist
Hyperparameter Optimization

Shuai Zhang

Amazon Scientist
Recommender Systems

Yi Tay

Google Senior Scientist
Recommender Systems

Framework Adaptation Authors

Anirudh Dagar

Amazon Scientist
PyTorch Adaptation
JAX 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
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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}
}

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