about this book
This book aims to provide the foundations of deep learning with PyTorch and demonstrate them in action through a real-life project. We strive to provide the key concepts underlying deep learning and show how PyTorch puts them in the hands of practitioners. In the book, we try to provide intuition that will support further exploration, and in doing so, we selectively delve into details to show what is going on behind the curtain.
Deep Learning with PyTorch doesn’t try to be a reference book; rather, it’s a conceptual companion that will allow you to explore more advanced material independently online. As such, we focus on a subset of the features offered by PyTorch.
Who should read this book
This book is intended for developers who are, or aim to become, deep learning practitioners and want to get acquainted with PyTorch. We imagine our typical reader to be a computer scientist, data scientist, or software engineer, or an undergraduate or graduate student in a related program. Since we don’t assume prior knowledge of deep learning, some parts in the first half of the book may be a repetition of concepts that are already understood by experienced practitioners. For those readers, we hope the exposition will provide a slightly different angle to known topics.