PDF Download Introduction to Deep Learning (The MIT Press), by Eugene Charniak

PDF Download Introduction to Deep Learning (The MIT Press), by Eugene Charniak

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Introduction to Deep Learning (The MIT Press), by Eugene Charniak

PDF Download Introduction to Deep Learning (The MIT Press), by Eugene Charniak

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Review

“We have a choice of a variety of books on deep learning: books on the theory written by expert academics, and practical books written by programmers. This book gives you the best of both: Charniak is a prominent academic researcher who has been through every phase of artificial intelligence, often as a leader in ushering in a new phase. And he remains an active programmer who understands by doing. In this masterfully executed book he shows you what he has come to understand, allowing you to follow the code step by step, and also learn from his informed conclusions.”―Peter Norvig, Research Director, Google“A wonderful book filling the yawning gap between the existing comprehensive 'bible' (by Goodfellow, Bengio, and Courville) and the many books aimed at industry practitioners. This approachable volume provides clear, engaging writing describing the theory and practical implementation of the key deep learning algorithms across vision, NLP, and robotics. The carefully thought out, compact presentation gets surprisingly close to the current state-of-the-art in deep learning, making it an ideal textbook for students and others seeking an insightful introduction to deep learning.”―Chris Manning, Professor of Computer Science and Linguistics, Stanford University“Deep learning is a mainstream tool in artificial intelligence and machine learning. This introductory text prepares a beginner for entering this exciting area of deep learning.”―Dapeng Oliver Wu, Professor of Computer Science, University of Florida“Eugene Charniak is famous for his clear explanations of important but complicated topics in artificial intelligence. In this book, he illuminates deep learning, introducing the essential building blocks for those who want a thorough, intuitive, hands-on, and hype-free experience. It will be highly valuable for practitioners and students alike.”―Noah A. Smith, Associate Professor of Computer Science, University of Washington

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About the Author

Eugene Charniak is Professor of Computer Science at Brown University. He is the author of Statistical Language Learning (MIT Press) and other books.

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Product details

Series: The MIT Press

Hardcover: 192 pages

Publisher: The MIT Press (January 29, 2019)

Language: English

ISBN-10: 0262039516

ISBN-13: 978-0262039512

Product Dimensions:

7 x 0.8 x 9 inches

Shipping Weight: 1.2 pounds (View shipping rates and policies)

Average Customer Review:

5.0 out of 5 stars

1 customer review

Amazon Best Sellers Rank:

#19,780 in Books (See Top 100 in Books)

I highly recommend this text to anyone getting started with deep learning. It's a much quicker read than the standard Goodfellow et al text, which was really the only option for quite some time. This book has enough detail to get you started, and lots of practical advice on building real neural networks. It's a much more practical way to get started. If you read this and want more detail, you should move on to the Goodfellow book, but I think almost everyone should start here.

Bought for my husband. He loves it

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Introduction to Deep Learning (The MIT Press), by Eugene Charniak PDF

Introduction to Deep Learning (The MIT Press), by Eugene Charniak PDF

Introduction to Deep Learning (The MIT Press), by Eugene Charniak PDF

Introduction to Deep Learning (The MIT Press), by Eugene Charniak PDF