.. 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