intro.py

"""
**Learn the Basics** ||
`Quickstart <quickstart_tutorial.html>`_ ||
`Tensors <tensorqs_tutorial.html>`_ ||
`Datasets & DataLoaders <data_tutorial.html>`_ ||
`Transforms <transforms_tutorial.html>`_ ||
`Build Model <buildmodel_tutorial.html>`_ ||
`Autograd <autogradqs_tutorial.html>`_ ||
`Optimization <optimization_tutorial.html>`_ ||
`Save & Load Model <saveloadrun_tutorial.html>`_

Learn the Basics
===================

Authors:
`Suraj Subramanian <https://github.com/subramen>`_,
`Seth Juarez <https://github.com/sethjuarez/>`_,
`Cassie Breviu <https://github.com/cassiebreviu/>`_,
`Dmitry Soshnikov <https://soshnikov.com/>`_,
`Ari Bornstein <https://github.com/aribornstein/>`_

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 <https://pytorch.org/get-started/locally/>`_). 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 <quickstart_tutorial.html>`_ 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 <tensor_tutorial.html>`_.


.. include:: /beginner_source/basics/qs_toc.txt

.. toctree::
   :maxdepth: 2
   :hidden:

   quickstart_tutorial
   tensorqs_tutorial
   data_tutorial
   transforms_tutorial
   buildmodel_tutorial
   autogradqs_tutorial
   optimization_tutorial
   saveloadrun_tutorial

"""