Deep Learning Architectures: A Mathematical Approach

Deep Learning Architectures: A Mathematical Approach Front Cover
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761 pages

Book Description

This book describes how neural operate from the point of view. As a result, neural networks can be interpreted both as function universal approximators and processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern language, presenting the best practices of the former and enjoying the robustness and elegance of the latter.

This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates.  In addition, the book will be of wide interest to researchers who are interested in a theoretical understanding of the subject.

Book Details

  • Title: Deep Learning Architectures: A Mathematical Approach
  • Author:
  • Length: 761 pages
  • Edition: 1st ed. 2020
  • Language: English
  • Publisher:
  • Publication Date: 2020-05-07
  • ISBN-10: 3030367207
  • ISBN-13: 9783030367206
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