diff --git a/beginner_source/deep_learning_60min_blitz.rst b/beginner_source/deep_learning_60min_blitz.rst index 4fc156c08ce..abe10156dcd 100644 --- a/beginner_source/deep_learning_60min_blitz.rst +++ b/beginner_source/deep_learning_60min_blitz.rst @@ -20,14 +20,12 @@ Goal of this tutorial: - Understand PyTorch’s Tensor library and neural networks at a high level. - Train a small neural network to classify images - -.. Note:: - Make sure you have the `torch`_ and `torchvision`_ packages installed. +To run the tutorials below, make sure you have the `torch`_ and `torchvision`_ +packages installed. .. _torch: https://github.com/pytorch/pytorch .. _torchvision: https://github.com/pytorch/vision - .. toctree:: :hidden: @@ -36,18 +34,33 @@ Goal of this tutorial: /beginner/blitz/neural_networks_tutorial /beginner/blitz/cifar10_tutorial -.. galleryitem:: /beginner/blitz/tensor_tutorial.py - :figure: /_static/img/tensor_illustration_flat.png +.. grid:: 4 -.. galleryitem:: /beginner/blitz/autograd_tutorial.py - :figure: /_static/img/autodiff.png + .. grid-item-card:: :octicon:`file-code;1em` Tensors + :link: /beginner/blitz/tensor_tutorial.html -.. galleryitem:: /beginner/blitz/neural_networks_tutorial.py - :figure: /_static/img/mnist.png + In this tutorial, you will learn the basics of PyTorch tensors. + +++ + :octicon:`code;1em` Code -.. galleryitem:: /beginner/blitz/cifar10_tutorial.py - :figure: /_static/img/cifar10.png + .. grid-item-card:: :octicon:`file-code;1em` A Gentle Introduction to torch.autograd + :link: /beginner/blitz/autograd_tutorial.html -.. raw:: html + Learn about autograd. + +++ + :octicon:`code;1em` Code + + .. grid-item-card:: :octicon:`file-code;1em` Neural Networks + :link: /beginner/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: /beginner/blitz/cifar10_tutorial.html -
+ Learn how to train an image classifier in PyTorch by using the + CIFAR10 dataset. + +++ + :octicon:`code;1em` Code