This project is adapted from the original Dive Into Deep Learning book by Aston Zhang, Zachary C. Lipton, Mu Li, Alex J. Smola and all the community contributors. GitHub of the original book: https://github.com/d2l-ai/d2l-en. We have made an effort to modify the book and convert the MXnet code snippets into PyTorch and Tensorflow.
Note: Some ipynb notebooks may not be rendered perfectly in Github. We suggest cloning the repo or using nbviewer to view the notebooks.
🏷️chap_installation
Please follow the INSTALL.md to acquire all the tools and the code of the book which is necessary to begin our course.
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Please feel free to open a Pull Request to contribute a notebook in PyTorch or tensorflow for the rest of the chapters. Before starting out with the notebook, open an issue with the name of the notebook in order to contribute for the same. We will assign that issue to you (if no one has been assigned earlier).
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Strictly follow the naming conventions for the IPython Notebooks and the subsections.
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Also, if you think there's any section that requires more/better explanation, please use the issue tracker to open an issue and let us know about the same. We'll get back as soon as possible.
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Find some code that needs improvement and submit a pull request.
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Find a reference that we missed and submit a pull request.
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Try not to submit huge pull requests since this makes them hard to understand and incorporate. Better send several smaller ones.
If you like this repo and find it useful, please consider (★) starring it, so that it can reach a broader audience.
[1] Original Book Dive Into Deep Learning -> Github Repo
[2] Deep Learning - The Straight Dope
[3] PyTorch - MXNet Cheatsheet
If you use this work or code for your research please cite the original book with the following bibtex entry.
@book{zhang2020dive,
title={Dive into Deep Learning},
author={Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola},
note={\url{https://d2l.ai}},
year={2020}
}