Training neural networks effectively requires adjusting model parameters to minimize a loss function, often using gradient descent or its variants. The core of this process is computing the gradient of the loss with respect to each parameter, mathematically represented as $\frac{\partial L}{\partial w}$ for a loss $L$ and parameter $w$. Manually deriving and implementing these gradient calculations for complex models is impractical.

This chapter introduces PyTorch's automatic differentiation engine, Autograd, designed to compute these gradients automatically. We will look at how PyTorch dynamically builds computation graphs as operations are performed on tensors. You'll learn how to flag tensors for gradient computation using requires_grad=True, trigger the backward pass to calculate gradients using .backward(), and inspect the resulting gradients stored in the .grad attribute. We will also cover gradient accumulation, the importance of zeroing gradients with optimizer.zero_grad(), and how to temporarily disable gradient calculations using contexts like torch.no_grad() for efficiency during inference or evaluation.