Learn the Basics — PyTorch Tutorials 2.15.0+cu132 documentation

Getting Started with PyTorch
Go to the end to download the full example code.
Learn the Basics || Quickstart || Tensors || Datasets & DataLoaders || Transforms || Build Model || Autograd || Optimization || Save & Load Model
Created On: Feb 09, 2021 | Last Updated: Jan 20, 2026 | Last Verified: Nov 05, 2024
Authors: Suraj Subramanian, Seth Juarez, Cassie Breviu, Dmitry Soshnikov, Ari Bornstein
Most machine learning workflows involve working with data, creating models, optimizing model parameters, and saving the trained models. This tutorial introduces you to a complete ML workflow implemented in PyTorch, with links to learn more about each of these concepts.
We’ll use the FashionMNIST dataset to train a neural network that predicts if an input image belongs to one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, or Ankle boot.
This tutorial assumes a basic familiarity with Python and Deep Learning concepts.
You can run this tutorial in a couple of ways:
In the cloud: This is the easiest way to get started! Each section has a “Run in Google Colab” link at the top, which opens an integrated notebook in Google Colab with the code in a fully-hosted environment.
Locally: This option requires you to set up PyTorch and TorchVision first on your local machine (installation instructions). Download the notebook or copy the code into your favorite IDE.
If you’re familiar with other deep learning frameworks, check out the 0. Quickstart first to quickly familiarize yourself with PyTorch’s API.
If you’re new to deep learning frameworks, head right into the first section of our step-by-step guide: 1. Tensors.
Download Jupyter notebook: intro.ipynb
Download Python source code: intro.py
Download zipped: intro.zip
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