You have learned how to define models using torch.nn and prepare data efficiently using Dataset and DataLoader. This chapter focuses on integrating these components to actually train your neural networks. We will construct the standard iterative process known as the training loop.

You will learn the essential steps involved:

• Setting up the model, loss function, and optimizer.
• Iterating through data batches provided by a DataLoader.
• Performing the forward pass: feeding input data to the model to get predictions.
• Calculating the loss: measuring the difference between model predictions and true labels using a chosen criterion, often denoted as $L$.
• Executing backpropagation: computing the gradients of the loss with respect to the model's parameters ($\nabla_{\theta} L$) by calling `$loss.backward()$.
• Updating model weights: adjusting the model's parameters ($\theta$) based on the computed gradients using an optimizer, typically via `$optimizer.step()$.
• Zeroing gradients before the next iteration using `$optimizer.zero_grad()$.

Additionally, we will cover how to implement a separate loop for evaluating model performance on validation or test data and methods for saving and loading model states (checkpoints) during or after training. By the end of this chapter, you'll be able to implement a complete training and evaluation routine for your PyTorch models.