PyTorch Tutorial

Short PyTorch code examples for learning deep learning models, from basics to GANs.

Stars: 32.5k
Forks: 8.2k
Licence: MIT
Language: Python
Last update: 3.2 years ago
Type: Learning resource

No commits in over a year — check for forks or alternatives before adopting.

What it is

This repository is a set of tutorial code for learning PyTorch, a Python library for building neural networks. It covers topics from basic tensors and regression through convolutional networks, RNNs, GANs, and image captioning, with most models described as implemented in under 30 lines of code. The README recommends finishing the official PyTorch tutorial first.

Run the basic PyTorch example to learn tensors and autograd
Train a convolutional neural network on an image dataset
Explore a GAN or variational autoencoder implementation
Study the TensorBoard utility example for logging training

Great for

Researchers or engineers who already know Python and want to learn PyTorch
People who prefer reading and running small, self-contained model examples
Learners who want to see common architectures like CNNs, RNNs, and VAEs in compact code

Not ideal for

Complete beginners to Python or programming, since the README points to the official PyTorch tutorial as a prerequisite
People who need up-to-date PyTorch APIs, since the README lists only PyTorch 0.4.0+ and the last push was in August 2023

Step-by-step guide: how to use PyTorch TutorialInstall, first run, examples, tips and FAQs

How to get it

1
Clone and build from source
You'll need: Python 2.7 or 3.5+ (per README) · PyTorch 0.4.0+ (per README)
Clone the repository
git clone https://github.com/yunjey/pytorch-tutorial.git
Change into a project folder
cd pytorch-tutorial/tutorials/PATH_TO_PROJECT
Run an example
python main.py
2
Run it
- Clone the repository with git.
- Pick a tutorial folder under tutorials/ such as 01-basics.
- Replace PATH_TO_PROJECT in the cd command with that folder path.
- Run python main.py inside the folder.
3
Get the most out of it
- Work through the tutorials in the order listed in the table of contents: Basics, Intermediate, Advanced, Utilities
- Read the linked main.py line ranges to see the core model code for each example
- Use the TensorBoard utility example to visualize training metrics
- Check the official PyTorch tutorial first, as the README recommends

Downloads

Source code (.zip)master branch · straight from GitHub

Strengths

Short, focused examples that are easy to read side by side
Covers a broad range of common model types in one place
Released under the permissive MIT license

Trade-offs

The README lists PyTorch 0.4.0+ and Python 2.7 or 3.5+, which are very old versions compared with current PyTorch releases
Last code push was 2023-08-15 and there are no releases, so maintenance status is unclear
Setup instructions are minimal; each example is run manually from its own folder

How it compares

Project	Stars	Type	Level	Updated	Best for
pytorch-tutorial (this)	32.5k	Learning resource	intermediate	3.2 years ago	Researchers or engineers who already know Python and want to learn PyTorch
annotated_deep_learning_paper_implementations	67.6k	Learning resource	intermediate	4 days ago	Students learning how transformers, GANs, or optimizers work internally
onnxruntime	22.2k	Library	intermediate	4 days ago	Developers who want to deploy a trained model inside an application or service
petals	10.7k	Library	intermediate	2.2 years ago	Developers who want to experiment with large open models like Llama 3.1 or BLOOM without owning the hardware to run them alone
Made-With-ML	49.9k	Learning resource	intermediate	8 months ago	Software engineers who want to learn how ML models get deployed and maintained
pytorch-deep-learning	19.3k	Learning resource	beginner	1 months ago	Beginners in machine learning who want to learn PyTorch in a hands-on, code-first way

Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.