Fundamentals of Deep Learning, 2nd Edition

Preface

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated field. Large companies such as Google, Microsoft, and Facebook have taken notice and are actively growing in-house deep learning teams. For the rest of us, deep learning is still a pretty complex and difficult subject to grasp. Research papers are filled to the brim with jargon, and scattered online tutorials do little to help build a strong intuition for why and how deep learning practitioners approach problems. Our goal is to bridge this gap.

In this second edition, we provide more rigorous background sections in mathematics with the aim of better equipping you for the material in the rest of the book. In addition, we have updated chapters in sequence analysis, computer vision, and reinforcement learning with deep dives into the latest advancements in the fields. And finally, we have added new chapters in the fields of generative modeling and interpretability to provide you with a broader view of the field of deep learning. We hope that these updates inspire you to practice deep learning on their own and apply their learnings to solve meaningful problems in the real world.

Prerequisites and Objectives

This book is aimed at an audience with a basic operating understanding of calculus and Python programming. ...