If you're looking for a book where you can learn about Deep Learning and PyTorch without having to spend hours deciphering cryptic text and code, and that's easy and enjoyable to read, this is it. This is not a typical book: most tutorials start with some nice and pretty image classification problem to illustrate how to use PyTorch. It may seem cool, but I believe it distracts you from the main goal: how PyTorch works? In this book, I present a structured, incremental, and from first principles approach to learn PyTorch (and get to the pretty image classification problem in due time). PyTorch is the fastest-growing framework for developing Deep Learning models and it has a huge ecosystem. That is, there are many tools and libraries developed on top of PyTorch. It is the preferred framework in academia already and is making its way in the industry. This book aims to get you started with PyTorch while giving you a solid understanding of how it works. In this book, I will guide you through the development of many models in PyTorch, showing you why PyTorch makes it much easier and more intuitive to build models in Python: autograd, dynamic computation graph, model classes and much, much more. I wrote this book for beginners in general—not only PyTorch beginners. Every now and then, I will spend some time explaining some fundamental concepts that I believe are essential to have a proper understanding of what’s going on in the code.