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books
| book details |
Deep Learning through Sparse and Low-Rank Modeling
By (author) Zhangyang Wang, By (author) Yu Fu, By (author) Thomas S. Huang
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| on special |
normal price: R 4 283.95
Price: R 3 855.95
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| book description |
Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.
| product details |
Normally shipped |
Publisher | Elsevier Science Publishing Co Inc
Published date | 12 Apr 2019
Language |
Format | Paperback / softback
Pages | 296
Dimensions | 235 x 191 x 0mm (L x W x H)
Weight | 570g
ISBN | 978-0-1281-3659-1
Readership Age |
BISAC | computers / computer vision
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Matt Dinniman
Paperback / softback
480 pages
was: R 514.95
now: R 452.95
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An epic love story with the pulse of a thriller that asks: what would you risk for a second chance at first love?
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