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.
This booked is aimed an audience with a basic operating understanding of calculus, matrices, and Python programming. Approaching this material without this background is possible, but likely to be more challenging. Background in linear algebra may also be helpful in navigating certain sections of mathematical exposition.
By the end of the book, we hope that our readers 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 TensorFlow open source library.
The following typographical conventions are used in this book:
Indicates new terms, URLs, email addresses, filenames, and file extensions.
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.
Constant width bold
Shows commands or other text that should be typed literally by the user.
Constant width italic
Shows text that should be replaced with user-supplied values or by values determined by context.
Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/darksigma/Fundamentals-of-Deep-Learning-Book.
This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.
We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Fundamentals of Deep Learning by Nikhil Buduma and Nicholas Locascio (O’Reilly). Copyright 2017 Nikhil Buduma and Nicholas Locascio, 978-1-491-92561-4.”
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We’d like to thank several people who have been instrumental in the completion of this text. We’d like to start by acknowledging Mostafa Samir and Surya Bhupatiraju, who contributed heavily to the content of Chapters 7 and 8. We also appreciate the contributions of Mohamed (Hassan) Kane and Anish Athalye, who worked on early versions of the code examples in this book’s Github repository.
This book would not have been possible without the never-ending support and expertise of our editor, Shannon Cutt. We’d also like to appreciate the commentary provided by our reviewers, Isaac Hodes, David Andrzejewski, and Aaron Schumacher, who provided thoughtful, in-depth commentary on the original drafts of the text. Finally, we are thankful for all of the insight provided by our friends and family members, including Jeff Dean, Nithin Buduma, Venkat Buduma, and William, Jack, as we finalized the manuscript of the text.