updated files

.gitignore

@@ -21,4 +21,5 @@ yarn-error.log*
 
 /tutorials/*.md
 /tutorials/data
+/tutorials/*.pth
 

docs/01-Introduction.md

@@ -0,0 +1,43 @@
+**Learn the Basics** ||
+[Quickstart](Quickstart.html) ||
+[Tensors](Tensors.html) ||
+[Datasets & DataLoaders](Data.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/suraj813),
+[Seth Juarez](https://github.com/sethjuarez/),
+[Cassie Breviu](https://github.com/cassieview/),
+[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" 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).
+