.. customcarditem::

:header: Loading data in PyTorch

:card_description: Learn how to use PyTorch packages to prepare and load common datasets for your model.

:link: ../recipes/recipes/loading_data_recipe.html

:tags: Basics

.. customcarditem::

:header: Defining a Neural Network

:card_description: Learn how to use PyTorch's torch.nn package to create and define a neural network the MNIST dataset.

:link: ../recipes/recipes/defining_a_neural_network.html

:tags: Basics

.. customcarditem::

:header: What is a state_dict in PyTorch

:card_description: Learn how state_dict objects, Python dictionaries, are used in saving or loading models from PyTorch.

:link: ../recipes/recipes/what_is_state_dict.html

:tags: Basics

.. customcarditem::

:header: Saving and loading models for inference in PyTorch

:card_description: Learn about the two approaches for saving and loading models for inference in PyTorch - via the state_dict and via the entire model.

:link: ../recipes/recipes/saving_and_loading_models_for_inference.html

:tags: Basics

.. customcarditem::

:header: Saving and loading a general checkpoint in PyTorch

:card_description: Saving and loading a general checkpoint model for inference or resuming training can be helpful for picking up where you last left off. In this recipe, explore how to save and load multiple checkpoints.

:link: ../recipes/recipes/saving_and_loading_a_general_checkpoint.html

:tags: Basics

.. customcarditem::

:header: Saving and loading multiple models in one file using PyTorch

:card_description: In this recipe, learn how saving and loading multiple models can be helpful for reusing models that you have previously trained.

:link: ../recipes/recipes/saving_multiple_models_in_one_file.html

:tags: Basics

.. customcarditem::

:header: Warmstarting model using parameters from a different model in PyTorch

:card_description: Learn how warmstarting the training process by partially loading a model or loading a partial model can help your model converge much faster than training from scratch.

:link: ../recipes/recipes/warmstarting_model_using_parameters_from_a_different_model.html

:tags: Basics

.. customcarditem::

:header: Saving and loading models across devices in PyTorch

:card_description: Learn how saving and loading models across devices (CPUs and GPUs) is relatively straightforward using PyTorch.

:link: ../recipes/recipes/save_load_across_devices.html

:tags: Basics

.. customcarditem::

:header: Zeroing out gradients in PyTorch

:card_description: Learn when you should zero out graidents and how doing so can help increase the accuracy of your model.

:link: ../recipes/recipes/zeroing_out_gradients.html

:tags: Basics

.. customcarditem::

:header: Custom Datasets, Transforms & Dataloaders

:card_description: Learn how to leverage the PyTorch dataset API to easily create a custom dataset and custom dataloader.

:link: ../recipes/recipes/custom_dataset_transforms_loader.html

:tags: Data-Customization

.. customcarditem::

:header: Model Interpretability using Captum

:card_description: Learn how to use Captum attribute the predictions of an image classifier to their corresponding image features and visualize the attribution results.

:link: ../recipes/recipes/Captum_Recipe.html

:tags: Interpretability, Captum

.. customcarditem::

:header: How to use TensorBoard with PyTorch

:card_description: Learn basic usage of TensorBoard with PyTorch, and how to visualize data in TensorBoard UI

:link: ../recipes/recipes/tensorboard_with_pytorch.html

:tags: Interpretability, TensorBoard

.. customcarditem::

:header: Dynamic Quantization

:card_description: Apply dynamic quantization to a simple LSTM model.

:link: ../recipes/recipes/dynamic_quantization.html

:tags: Quantization, Text, Model-Optimization

.. customcarditem::

:header: TorchScript for Deployment

:card_description: Learn how to export your trained model in TorchScript format and how to load your TorchScript model in C++ and do inference.

:link: ../recipes/recipes/torchscript_inference.html

:tags: TorchScript

.. customcarditem::

:header: Deploying with Flask

:card_description: Learn how to use Flask, a lightweight web server, to quickly setup a web API from your trained PyTorch model.

:link: ../recipes/recipes/deployment_with_flask.html

:tags: Production, TorchScript

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