Part 1 Core PyTorch
Welcome to the first part of this book. This is where we’ll take our first steps with PyTorch, gaining the fundamental skills needed to understand its anatomy and work out the mechanics of a PyTorch project.
Chapter 1 introduces PyTorch, explains what it is, what problems it solves, and how it compares to other deep learning frameworks. Chapter 2 gives us a hands-on tour with pretrained models on interesting tasks. Chapter 3 gets a bit more serious and teaches the basic data structure used in PyTorch programs: the tensor. Chapter 4 will take us on another tour, this time across ways to represent data from different domains as PyTorch tensors. Chapter 5 unveils how a program can learn from examples and how PyTorch supports this process. Chapter 6 provides the fundamentals of what a neural network is and how to build a neural network with PyTorch. Chapter 7 tackles a simple image classification problem with a neural network architecture. Finally, chapter 8 shows how the same problem can be cracked in a much smarter way using a convolutional neural network.
By the end of part 1 (chapters 1–8), we’ll have what it takes to tackle real-world problems with PyTorch in part 2.