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diff --git a/.ipynb_checkpoints/intro-checkpoint.ipynb b/.ipynb_checkpoints/intro-checkpoint.ipynb new file mode 100644 index 0000000..95ff70a --- /dev/null +++ b/.ipynb_checkpoints/intro-checkpoint.ipynb @@ -0,0 +1,43 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n**Learn the Basics** ||\n`Quickstart <quickstart_tutorial.html>`_ ||\n`Tensors <tensorqs_tutorial.html>`_ ||\n`Datasets & DataLoaders <data_tutorial.html>`_ ||\n`Transforms <transforms_tutorial.html>`_ ||\n`Build Model <buildmodel_tutorial.html>`_ ||\n`Autograd <autogradqs_tutorial.html>`_ ||\n`Optimization <optimization_tutorial.html>`_ ||\n`Save & Load Model <saveloadrun_tutorial.html>`_\n\nLearn the Basics\n===================\n\nAuthors:\n`Suraj Subramanian <https://github.com/suraj813>`_,\n`Seth Juarez <https://github.com/sethjuarez/>`_,\n`Cassie Breviu <https://github.com/cassieview/>`_,\n`Dmitry Soshnikov <https://soshnikov.com/>`_,\n`Ari Bornstein <https://github.com/aribornstein/>`_\n\nMost machine learning workflows involve working with data, creating models, optimizing model\nparameters, and saving the trained models. This tutorial introduces you to a complete ML workflow\nimplemented in PyTorch, with links to learn more about each of these concepts.\n\nWe'll use the FashionMNIST dataset to train a neural network that predicts if an input image belongs\nto one of the following classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker,\nBag, or Ankle boot.\n\n`This tutorial assumes a basic familiarity with Python and Deep Learning concepts.`\n\n\nRunning the Tutorial Code\n------------------\nYou can run this tutorial in a couple of ways:\n\n- **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.\n- **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.\n\n\nHow to Use this Guide\n-----------------\nIf you're familiar with other deep learning frameworks, check out the `0. Quickstart <quickstart_tutorial.html>`_ first\nto quickly familiarize yourself with PyTorch's API.\n\nIf you're new to deep learning frameworks, head right into the first section of our step-by-step guide: `1. Tensors <tensor_tutorial.html>`_.\n\n\n.. include:: /beginner_source/basics/qs_toc.txt\n\n.. toctree::\n :hidden:\n\n\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}
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