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2021 Deep lowest Learning: wholesale A Visual Approach online

2021 Deep lowest Learning: wholesale A Visual Approach online
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'Best Yet'

"Andrew is famous for his ability to teach complex topics that blend mathematics and algorithms, and this work I think is his best yet."

—Peter Shirley, Distinguished Research Engineer, Nvidia


'Read It Cover-to-Cover'

“I would recommend that anyone entering this area, or even already familiar with the subject, read it cover-to-cover to firmly ground their understanding.“

—Richard Szeliski, author of Computer Vision: Algorithms and Applications


'Great Introduction to Deep Learning'

"This book is a great introduction to machine learning, in general, and more specifically to deep learning (neural networks). The author thoroughly explains each concept using pictures instead of math."

—Mike, Amazon reviewer

About The Author

Dr. Andrew Glassner is a Senior Research Scientist at Weta Digital, where he uses deep learning to help artists produce visual effects for film and television. He was Technical Papers Chair for SIGGRAPH ’94, Founding Editor of the Journal of Computer Graphics Tools, and Editor-in-Chief of ACM Transactions on Graphics. His prior books include the Graphics Gems series and the textbook Principles of Digital Image Synthesis. Glassner holds a PhD from UNC-Chapel Hill. He paints, plays jazz piano, and writes novels. He can be followed on Twitter as @AndrewGlassner.

Who Should Read This Book

You don’t need math or programming experience. You don’t need to be a computer whiz. You don’t have to be a technologist at all!

This book is for anyone with curiosity and a desire to look behind the headlines. You may be surprised that most of the algorithms of deep learning aren’t very complicated or hard to understand. They’re usually simple and elegant and gain their power by being repeated millions of times over huge databases.

In addition to satisfying pure intellectual curiosity, Glassner wrote this book for people who come face to face with deep learning, either in their own work or when interacting with others who use it. After all, one of the best reasons to understand AI is so we can use it ourselves! We can build AI systems now that help us do our work better, enjoy our hobbies more deeply, and understand the world around us more fully.

If you want to know how this stuff works, you’re going to feel right at home.

About the Publisher

No Starch Press has published the finest in geek entertainment since 1994, creating both timely and timeless titles like Python Crash Course, Python for Kids, How Linux Works, and Hacking: The Art of Exploitation. An independent, San Francisco-based publishing company, No Starch Press focuses on a curated list of well-crafted books that make a difference. They publish on many topics, including computer programming, cybersecurity, operating systems, and LEGO. The titles have personality, the authors are passionate experts, and all the content goes through extensive editorial and technical reviews. Long known for its fun, fearless approach to technology, No Starch Press has earned wide support from STEM enthusiasts worldwide.

Description

Product Description

A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You''ll learn how to use key deep learning algorithms without the need for complex math.

Ever since computers began beating us at chess, they''ve been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare.

Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books - the possibilities are endless.

Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you''re ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going.

The book''s conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including:
 
     How text generators create novel stories and articles
     How deep learning systems learn to play and win at human games
     How image classification systems identify objects or people in a photo
     How to think about probabilities in a way that''s useful to everyday life
     How to use the machine learning techniques that form the core of modern AI

Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It''s the future of AI, and this book allows you to fully envision it.
 
Full Color Illustrations

Review

"Andrew is famous for his ability to teach complex topics that blend mathematics and algorithms, and this work I think is his best yet."
Peter Shirley, Distinguished Research Engineer, Nvidia

“I would recommend that anyone entering this area, or even already familiar with the subject, read it cover-to-cover to firmly ground their understanding.“
Richard Szeliski, author of Computer Vision: Algorithms and Applications

"This is a comprehensive—yet easy to understand—book about complex concepts and algorithms. Andrew Glassner demonstrates that visualizing concepts as graphs is a tremendous benefit to easy cognition."
—Thomas Frisendal, author of Graph Data Modeling for NoSQL and SQL

About the Author

Andrew Glassner is a research scientist specializing in computer graphics and deep learning. He is currently a Senior Research Scientist at Weta Digital, where he works on integrating deep learning with the production of world-class visual effects for films and television. He has previously worked as a researcher at labs such as the IBM Watson Lab, Xerox PARC, and Microsoft Research. He was Editor in Chief of ACM TOG, the premier research journal in graphics, and Technical Papers Chair for SIGGRAPH, the premier conference in graphics. He''s written or edited a dozen technical books on computer graphics, ranging from the textbook Principles of Digital Image Synthesis to the popular Graphics Gems series, offering practical algorithms for working programmers. Glassner has a PhD in Computer Science from UNC-Chapel Hill.

Product information

2021 Deep lowest Learning: wholesale A Visual Approach online

2021 Deep lowest Learning: wholesale A Visual Approach online

2021 Deep lowest Learning: wholesale A Visual Approach online

2021 Deep lowest Learning: wholesale A Visual Approach online

2021 Deep lowest Learning: wholesale A Visual Approach online

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