Learn the Basics || Quickstart || Tensors || Datasets & DataLoaders || Transforms || Build Model || Autograd || Optimization || Save & Load Model

Learn the Basics

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.

Running the Tutorial Code

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 Microsoft Learn” and “Run in Google Colab” link at the top, which opens an integrated notebook in Microsoft Learn or Google Colab, respectively, with the code in a fully-hosted environment.

  • Locally: This option requires you to setup PyTorch and TorchVision first on your local machine (installation instructions). Download the notebook or copy the code into your favorite IDE.

How to Use this Guide

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.

Total running time of the script: ( 0 minutes 0.000 seconds)

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