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Deep Learning with PyTorch: A 60 Minute Blitz --------------------------------------------- **Author**: `Soumith Chintala <http://soumith.ch>`_ .. raw:: html <div style="margin-top:10px; margin-bottom:10px;"> <iframe width="560" height="315" src="https://www.youtube.com/embed/u7x8RXwLKcA" frameborder="0" allow="accelerometer; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> </div> What is PyTorch? ~~~~~~~~~~~~~~~~~~~~~ PyTorch is a Python-based scientific computing package serving two broad purposes: - A replacement for NumPy to use the power of GPUs and other accelerators. - An automatic differentiation library that is useful to implement neural networks. Goal of this tutorial: ~~~~~~~~~~~~~~~~~~~~~~~~ - Understand PyTorch’s Tensor library and neural networks at a high level. - Train a small neural network to classify images To run the tutorials below, make sure you have the `torch`_, `torchvision`_, and `matplotlib`_ packages installed. .. _torch: https://github.com/pytorch/pytorch .. _torchvision: https://github.com/pytorch/vision .. _matplotlib: https://github.com/matplotlib/matplotlib .. toctree:: :hidden: /beginner/blitz/tensor_tutorial /beginner/blitz/autograd_tutorial /beginner/blitz/neural_networks_tutorial /beginner/blitz/cifar10_tutorial .. grid:: 4 .. grid-item-card:: :octicon:`file-code;1em` Tensors :link: blitz/tensor_tutorial.html In this tutorial, you will learn the basics of PyTorch tensors. +++ :octicon:`code;1em` Code .. grid-item-card:: :octicon:`file-code;1em` A Gentle Introduction to torch.autograd :link: blitz/autograd_tutorial.html Learn about autograd. +++ :octicon:`code;1em` Code .. grid-item-card:: :octicon:`file-code;1em` Neural Networks :link: blitz/neural_networks_tutorial.html This tutorial demonstrates how you can train neural networks in PyTorch. +++ :octicon:`code;1em` Code .. grid-item-card:: :octicon:`file-code;1em` Training a Classifier :link: blitz/cifar10_tutorial.html Learn how to train an image classifier in PyTorch by using the CIFAR10 dataset. +++ :octicon:`code;1em` Code