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(Ebook) Deep Learning Through Sparse and Low-Rank Modeling by Zhangyang Wang; Yun Raymond; Thomas S Huang ISBN 9780128136591, 0128136596

  • SKU: EBN-9968724
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$ 32 $ 40 (-20%)

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4.5

41 reviews
Instant download (eBook) Deep Learning Through Sparse and Low-Rank Modeling after payment.
Authors:Zhangyang Wang; Yun Raymond; Thomas S Huang
Pages:296 pages.
Year:2019
Editon:Paperback
Publisher:Academic Press
Language:english
File Size:17.79 MB
Format:pdf
ISBNS:9780128136591, 0128136596
Categories: Ebooks

Product desciption

(Ebook) Deep Learning Through Sparse and Low-Rank Modeling by Zhangyang Wang; Yun Raymond; Thomas S Huang ISBN 9780128136591, 0128136596

Deep Learning through Sparse Representation and Low-Rank Modelingbridges 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.Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networksShows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning modelsProvides tactics on how to build and apply customized deep learning models for various applications
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