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Toward Deep Neural Networks
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Toward Deep Neural Networks : WASD Neuronet Models, Algorithms, and Applications

Book Details

Format Hardback or Cased Book
ISBN-10 1138387037
ISBN-13 9781138387034
Publisher Taylor & Francis Ltd
Imprint CRC Press
Country of Manufacture GB
Country of Publication GB
Publication Date Mar 20th, 2019
Print length 368 Pages
Weight 846 grams
Dimensions 18.40 x 26.00 x 2.50 cms
Ksh 24,300.00
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This book introduces deep neural networks, with a focus on the weights-and-structure determination (WASD) algorithm. Based on the authors’ 20 years of research experience on neuronets, the book explores the models, algorithms, and applications of the WASD neuronet.

Toward Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications introduces the outlook and extension toward deep neural networks, with a focus on the weights-and-structure determination (WASD) algorithm. Based on the authors’ 20 years of research experience on neuronets, the book explores the models, algorithms, and applications of the WASD neuronet, and allows reader to extend the techniques in the book to solve scientific and engineering problems. The book will be of interest to engineers, senior undergraduates, postgraduates, and researchers in the fields of neuronets, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, simulation and modeling, deep learning, and data mining.

Features

  • Focuses on neuronet models, algorithms, and applications
  • Designs, constructs, develops, analyzes, simulates and compares various WASD neuronet models, such as single-input WASD neuronet models, two-input WASD neuronet models, three-input WASD neuronet models, and general multi-input WASD neuronet models for function data approximations
  • Includes real-world applications, such as population prediction
  • Provides complete mathematical foundations, such as Weierstrass approximation, Bernstein polynomial approximation, Taylor polynomial approximation, and multivariate function approximation, exploring the close integration of mathematics (i.e., function approximation theories) and computers (e.g., computer algorithms)
    • Utilizes the authors'' 20 years of research on neuronets

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