Building Neural Networks

Neural networks lie at the core of modern machine learning, driving advancements across domains like image recognition and natural language processing. In this chapter, you'll embark on a journey to grasp how these networks are constructed using PyTorch, leveraging its intuitive and dynamic architecture.

You'll begin by exploring the fundamental components that make up a neural network, including layers, activation functions, and weights. Comprehending these elements is crucial, as they govern how a network learns to map inputs to outputs. As you progress, you'll learn to design and assemble these components into functional models, crafting networks tailored to specific tasks.

The chapter will also guide you through implementing backpropagation in PyTorch, a critical algorithm for training neural networks. By the end, you'll be equipped with the skills to construct various neural network types, from simple feedforward models to more complex architectures. Along the way, you'll apply PyTorch's capabilities to streamline your workflow, making the process of building neural networks both accessible and efficient.

Prepare to deepen your understanding of neural networks and gain hands-on experience in bringing them to life with PyTorch.