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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
| Date | Stars |
|---|---|
| 2026-07-24 | 4357 |
| 2026-07-25 | 4358 |
| 2026-07-28 | 4359 |
| 2026-07-30 | 4359 |
| 2026-07-31 | 4361 |
| 2026-08-06 | 4359 |
Today
-2 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<p align="center">
<img width="60%" src="/img/d2l-pytorch.png" />
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**UPDATE: Please see the [orignal repo](https://github.com/d2l-ai/d2l-en) for the complete PyTorch port. We no longer maintain this repo.**
This project is adapted from the original [Dive Into Deep Learning](https://d2l.ai) 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](https://github.com/d2l-ai/d2l-en). We have made an effort to modify the book and convert the MXnet code snippets into PyTorch.
Note: Some ipynb notebooks may not be rendered perfectly in Github. We suggest `cloning` the repo or using [nbviewer](https://nbviewer.jupyter.org/) to view the notebooks.
## Chapters
* **Ch02 Installation**
* [Installation](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch02_Installation/INSTALL.md)
* **Ch03 Introduction**
* [Introduction](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch03_Introduction/Introduction.ipynb)
* **Ch04 The Preliminaries: A Crashcourse**
* 4.1 [Data Manipulation](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch04_The_Preliminaries_A_Crashcourse/Data_Manipulation.ipynb)
* 4.2 [Linear Algebra](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch04_The_Preliminaries_A_Crashcourse/Linear_Algebra.ipynb)
* 4.3 [Automatic Differentiation](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch04_The_Preliminaries_A_Crashcourse/Automatic_Differentiation.ipynb)
* 4.4 [Probability and Statistics](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch04_The_Preliminaries_A_Crashcourse/Probability_and_Statistics.ipynb)
* 4.5 [Naive Bayes Classification](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch04_The_Preliminaries_A_Crashcourse/Naive_Bayes_Classification.ipynb)
* 4.6 [Documentation](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch04_The_Preliminaries_A_Crashcourse/Documentation.ipynb)
* **Ch05 Linear Neural Networks**
* 5.1 [Linear Regression](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch05_Linear_Neural_Networks/Linear_Regression.ipynb)
* 5.2 [Linear Regression Implementation from Scratch](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch05_Linear_Neural_Networks/Linear_Regression_Implementation_from_Scratch.ipynb)
* 5.3 [Concise Implementation of Linear Regression](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch05_Linear_Neural_Networks/Concise_Implementation_of_Linear_Regression.ipynb)
* 5.4 [Softmax Regression](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch05_Linear_Neural_Networks/Softmax_Regression.ipynb)
* 5.5 [Image Classification Data (Fashion-MNIST)](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch05_Linear_Neural_Networks/Image_Classification_Data(Fashion-MNIST).ipynb)
* 5.6 [Implementation of Softmax Regression from Scratch](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch05_Linear_Neural_Networks/Implementation_of_Softmax_Regression_from_Scratch.ipynb)
* 5.7 [Concise Implementation of Softmax Regression](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch05_Linear_Neural_Networks/Concise_Implementation_of_Softmax_Regression.ipynb)
* **Ch06 Multilayer Perceptrons**
* 6.1 [Multilayer Perceptron](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch06_Multilayer_Perceptrons/Multilayer_Perceptron.ipynb)
* 6.2 [Implementation of Multilayer Perceptron from Scratch](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch06_Multilayer_Perceptrons/Implementation_of_Multilayer_Perceptron_from_Scratch.ipynb)
* 6.3 [Concise Implementation of Multilayer Perceptron](https://github.com/dsgiitr/d2l-pytorch/blob/master/Ch06_Multilayer_Perceptrons/Concise_Implementation_of_Multilayer_Perceptron.ipynb)
* 6.4 [Model Selection Underfitting and Overfitting](https://github.com/dExcerpt of 14,667 characters
Read on GitHub
Anirudh Dagar · @aws @autogluon @d2l-ai · Germany
107
14
13
Saswat Das · @dsgiitr
7
7
Subham Sahoo
7
Shashank Gupta
6
4
3
2
2
Akshit Mittal
2
2
Ishan Kumar · MS CS at Columbia University, Microsoft @microsoft, SDS Data Science Group @dsgiitr · United States
2
1
1
1
1
1
Akshay Subramanian · Massachusetts Institute of Technology
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cb070f6aec2fb79b, topic:deep-learning, topic:pytorch, readme:automatic differentiation
matched fp:cb070f6aec2fb79b, topic:computer-vision, readme:image classification
matched fp:cb070f6aec2fb79b, topic:nlp
matched fp:cb070f6aec2fb79b, topic:book