Fundamentals of Deep Learning
by Nithin Buduma, Nikhil Buduma , and Joe Papa
Copyright 2022 Nithin Buduma and Mobile Insights Technology Group, LLC. All rights reserved.
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- June 2017: First Edition
- May 2022: Second Edition
Revision History for the Second Edition
- 2022-05-16: First Release
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978-1-492-08218-7
LSI
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. In this latest edition, we provide extensive mathematical background chapters, specifically in linear algebra and probability, to prepare you for the material that lies ahead.
By the end of the book, we hope you will be left with an intuition for how to approach problems using deep learning, the historical context for modern deep learning approaches, and a familiarity with implementing deep learning algorithms using the PyTorch open source library.
How Is This Book Organized?
The first chapters of this book are dedicated to developing mathematical maturity via deep dives into linear algebra and probability, which are deeply embedded in the field of deep learning. The next several chapters discuss the structure of feed-forward neural networks, how to implement them in code, and how to train and evaluate them on real-world datasets. The rest of the book is dedicated to specific applications of deep learning and understanding the intuition behind the specialized learning techniques and neural network architectures developed for those applications. Although we cover advanced research in these latter sections, we hope to provide a breakdown of these techniques that is derived from first principles and digestible.
Conventions Used in This Book
The following typographical conventions are used in this book:
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Constant width
Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.
Note
This element signifies a general note.
Warning
This element indicates a warning or caution.
Using Code Examples
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