Convolutional Neural Networks in Visual Computing: A Concise Guide (Data-Enabled Engineering)
| Author | : | |
| Rating | : | 4.78 (558 Votes) |
| Asin | : | 1138747955 |
| Format Type | : | paperback |
| Number of Pages | : | 226 Pages |
| Publish Date | : | 2014-09-30 |
| Language | : | English |
DESCRIPTION:
From 2013 to 2014, Venkatesan was with the Intel Corporation as a computer vision research intern working on technologies for autonomous vehicles. degree in 2012. He is currently a Professor and Chair of the Computer Science and Engineering program, and a Graduate Faculty in Electrical Engineering and Computer Engineering programs at Arizona State University, Tempe. patents and his current research interests include computer vision and pattern recognition, multimedia, social computing, machine learning, and assistive technologies. He holds eighteen issued U.S. About the AuthorRagav Venkatesan is currently completing his Ph.D. He has been a Research Associate with the Visual Representation and Processing Group in ASU, and has worked as a Teaching Assistant for several graduate-level courses in machine learning, pattern recognition, video processing and computer vision. He was a recipient of the National Science Foundation’s CAREER Award. From 2000 to 2004, he was a Seni
He holds eighteen issued U.S. From 2000 to 2004, he was a Senior Researcher with SHARP Laboratories of America, Camas, Washington, where he was a technical lead in developing SHARP’s trademarked HiMPACT Sports technologies. study in Computer Science in the School of Computing, Informatics and Decision Systems Engineering at Arizona State University. patents and his current research interests include computer vision a
It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.. This book covers the fundamentals in designing and deploying techniques using deep architectures