about this book
This book was written for anyone who wishes to explore deep learning from scratch or broaden their understanding of deep learning. Whether you’re a practicing machine learning engineer, a software developer, or a college student, you’ll find value in these pages.
You’ll explore deep learning in an approachable way—starting simply and working up to state-of-the-art techniques. We hope you’ll find that this book strikes a balance between intuition, theory, and hands-on practice. It avoids mathematical notation, preferring instead to explain the core ideas of deep learning via functioning code paired with explanations of the underlying principles. You’ll train machine learning models from scratch in a number of different problem domains and learn practical recommendations for writing deep learning programs and deploying them in the real world.
After reading this book, you’ll have a solid understanding of what deep learning is, when it’s applicable, and what its limitations are. You’ll be familiar with the standard workflow for approaching and solving machine learning problems, and you’ll know how to address commonly encountered issues.
Who should read this book
This book is written for people with some Python programming experience who want to get started with machine learning and deep learning. But this book can also be valuable to many different types of readers: