Deep Learning through Sparse and Low Rank Modeling

Book Deep Learning through Sparse and Low Rank Modeling Cover

Download or read online Deep Learning through Sparse and Low Rank Modeling PDF book by Zhangyang Wang in ePUB, PDF or Kindle eBooks. Published by Academic Press in 26 April 2019 the book become immediate popular and critical acclaim in Computers books with total hardcover page 296. Click Download Book button to get book file. Read some info about this book below.

  • Author : Zhangyang Wang
  • Publisher : Academic Press
  • Release : 26 April 2019
  • ISBN : 9780128136591
  • Page : 296 pages
  • Language : English
  • Total View : 3715 Views
  • File Size : 47,6 Mb

Deep Learning through Sparse and Low Rank Modeling Book PDF summary

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. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications

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