Now that you understand how to build models using torch.nn and compute gradients with Autograd, the next step is feeding these models with data effectively. Handling large datasets, applying necessary preprocessing steps, and loading data in batches without running out of memory are common challenges in deep learning workflows.

This chapter focuses on PyTorch's solution for managing data pipelines: the torch.utils.data module. You will learn how to:

• Structure your data using the Dataset class.
• Use pre-built datasets, like those available in torchvision.
• Apply data transformations and augmentations with torchvision.transforms.
• Efficiently load data in batches, shuffle it, and potentially parallelize loading using the DataLoader class.

By the end of this chapter, you'll be able to build efficient data pipelines for your PyTorch projects.