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Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications (Chapman & Hall/CRC Artificial Intelligence and Robotics Series)

Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications (Chapman & Hall/CRC Artificial Intelligence and Robotics Series)

Dechao Chen
3/5 ( ratings)
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 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 and computers









Utilizes the authors' 20 years of research on neuronets
Pages
366
Format
Kindle Edition
Publisher
Chapman and Hall/CRC
Release
March 19, 2019

Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications (Chapman & Hall/CRC Artificial Intelligence and Robotics Series)

Dechao Chen
3/5 ( ratings)
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 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 and computers









Utilizes the authors' 20 years of research on neuronets
Pages
366
Format
Kindle Edition
Publisher
Chapman and Hall/CRC
Release
March 19, 2019

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