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Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
| Date | Stars |
|---|---|
| 2026-07-24 | 18480 |
| 2026-07-25 | 18507 |
| 2026-07-28 | 18507 |
| 2026-07-30 | 18507 |
| 2026-07-31 | 18588 |
| 2026-08-06 | 18628 |
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# Learn PyTorch for Deep Learning
Welcome to the [Zero to Mastery Learn PyTorch for Deep Learning course](https://dbourke.link/ZTMPyTorch), the second best place to learn PyTorch on the internet (the first being the [PyTorch documentation](https://pytorch.org/docs/stable/index.html)).
* **Update April 2023:** New [tutorial for PyTorch 2.0](https://www.learnpytorch.io/pytorch_2_intro/) is live! And because PyTorch 2.0 is an additive (new features) and backward-compatible release, all previous course materials will *still* work with PyTorch 2.0.
<div align="center">
<a href="https://learnpytorch.io">
<img src="https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/misc-pytorch-course-launch-cover-white-text-black-background.jpg" width=750 alt="pytorch deep learning by zero to mastery cover photo with different sections of the course">
</a>
</div>
## Contents of this page
* [Course materials/outline](https://github.com/mrdbourke/pytorch-deep-learning#course-materialsoutline)
* [About this course](https://github.com/mrdbourke/pytorch-deep-learning#about-this-course)
* [Status](https://github.com/mrdbourke/pytorch-deep-learning#status) (the progress of the course creation)
* [Log](https://github.com/mrdbourke/pytorch-deep-learning#log) (a log of the course material creation process)
## Course materials/outline
* 📖 **Online book version:** All of course materials are available in a readable online book at [learnpytorch.io](https://learnpytorch.io).
* 🎥 **First five sections on YouTube:** Learn PyTorch in a day by watching the [first 25 hours of material](https://youtu.be/Z_ikDlimN6A).
* 🔬 **Course focus:** code, code, code, experiment, experiment, experiment.
* 🏃♂️ **Teaching style:** [https://sive.rs/kimo](https://sive.rs/kimo).
* 🤔 **Ask a question:** See the [GitHub Discussions page](https://github.com/mrdbourke/pytorch-deep-learning/discussions) for existing questions/ask your own.
| **Section** | **What does it cover?** | **Exercises & Extra-curriculum** | **Slides** |
| ----- | ----- | ----- | ----- |
| [00 - PyTorch Fundamentals](https://www.learnpytorch.io/00_pytorch_fundamentals/) | Many fundamental PyTorch operations used for deep learning and neural networks. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/00_pytorch_fundamentals/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/00_pytorch_and_deep_learning_fundamentals.pdf) |
| [01 - PyTorch Workflow](https://www.learnpytorch.io/01_pytorch_workflow/) | Provides an outline for approaching deep learning problems and building neural networks with PyTorch. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/01_pytorch_workflow/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/01_pytorch_workflow.pdf) |
| [02 - PyTorch Neural Network Classification](https://www.learnpytorch.io/02_pytorch_classification/) | Uses the PyTorch workflow from 01 to go through a neural network classification problem. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/02_pytorch_classification/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/02_pytorch_classification.pdf) |
| [03 - PyTorch Computer Vision](https://www.learnpytorch.io/03_pytorch_computer_vision/) | Let's see how PyTorch can be used for computer vision problems using the same workflow from 01 & 02. | [Go to exercises & extra-curriculum](https://www.learnpytorch.io/03_pytorch_computer_vision/#exercises) | [Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/03_pytorch_computer_vision.pdf) |
| [04 - PyTorch Custom Datasets](https://www.learnpytorch.io/04_pytorch_custom_datasets/) | How do you load a custom dataset into PyTorch? Also we'll be laying the foundations in this notebook for our modular code (covered in 05). | [Go to exercises & extra-curriculum](https://www.learExcerpt of 24,223 characters
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