Deep Learning: A Visual Approach
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"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, 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.
Product details
| Main Specification | |
|---|---|
| Date de publication | 2021 or June 29 |
| Marque | by Andrew Glassner (Author) Format: Paperback |
| Poids de l'article | 3.85 pounds |
| Product information | |
| Langue | changements climatiques |
| Others | |
| Type de produit | Neural Networks |
| Édition | Illustré |
| Éditeur | No Starch Press |
| Longueur d'impression | 768 pages |
| L'objectif de l'initiative est de mettre en place un système d'échange d'informations entre les États membres. | 1718500726 |
| ISBN 13 | 978-1718500723 |
| Dimensions | 7.01 x 1.57 x 9.25 inches |
| ISBN 10 | 1718500726 |
| ISBN 13 | 978-1718500723 |