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Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse and Low-Rank Modeling Paperback - 2019

by Zhangyan Wang

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Paperback. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; 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 ca
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Details

  • Title Deep Learning through Sparse and Low-Rank Modeling
  • Author Zhangyan Wang
  • Binding Paperback
  • Condition New
  • Pages 296
  • Volumes 1
  • Language ENG
  • Publisher Academic Press
  • Publication date 2019-04-12
  • Features Bibliography, Index
  • Bookseller's Inventory # ria9780128136591_inp
  • ISBN 9780128136591 / 0128136596
  • Weight 1.13 lbs (0.51 kg)
  • Dimensions 9.25 x 7.5 x 0.62 in (23.50 x 19.05 x 1.57 cm)
  • Category Computers - Other Applications
  • Library of Congress subjects Data mining, Machine learning
  • Library of Congress Catalogue Number 2021277362
  • Quantity available 649

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From the publisher

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.

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