Having established a foundation with PyTorch Tensors and the Autograd system for gradient computation, we now turn to constructing the neural networks themselves. This chapter focuses on the torch.nn package, PyTorch's dedicated library for building network architectures efficiently.

You will learn to use the core nn.Module class as the blueprint for your models. We will assemble networks using common building blocks provided by PyTorch, including linear (nn.Linear), convolutional (nn.Conv2d), and recurrent (nn.RNN) layers. We'll integrate essential components like activation functions (e.g., ReLU, Sigmoid) to introduce non-linear processing capabilities. Furthermore, you'll learn how to define objective measures using loss functions from torch.nn (like $MSELoss$ or $CrossEntropyLoss$) and how to select an appropriate optimization algorithm from torch.optim (such as SGD or Adam) to iteratively refine your model's parameters during training. By the end of this chapter, you'll be able to define and instantiate your own basic neural networks in PyTorch.