<reponame>daniellepintz/tutorials
"""
**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: 
`<NAME> <https://github.com/suraj813>`_,
`<NAME> <https://github.com/sethjuarez/>`_, 
`<NAME> <https://github.com/cassieview/>`_, 
`<NAME> <https://soshnikov.com/>`_, 
`<NAME> <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" link at the top, which opens an integrated notebook in Microsoft Learn 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::
   :hidden:

"""
"""
**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: 
`<NAME> <https://github.com/suraj813>`_,
`<NAME> <https://github.com/sethjuarez/>`_, 
`<NAME> <https://github.com/cassieview/>`_, 
`<NAME> <https://soshnikov.com/>`_, 
`<NAME> <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" link at the top, which opens an integrated notebook in Microsoft Learn 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::
   :hidden:

"""
**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:  `<NAME> <https://github.com/suraj813>`_, `<NAME> <https://github.com/sethjuarez/>`_,  `<NAME> <https://github.com/cassieview/>`_,  `<NAME> <https://soshnikov.com/>`_,  `<NAME> <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" link at the top, which opens an integrated notebook in Microsoft Learn 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::    :hidden: