Deep Neural Networks
WASD Neuronet Models, Algorithms, and Applications
- 340 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Deep Neural Networks
WASD Neuronet Models, Algorithms, and Applications
About This Book
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
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- 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
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- Includes real-world applications, such as population prediction
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- 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)
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- Utilizes the authors' 20 years of research on neuronets
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Frequently asked questions
Information
Table of contents
- Cover
- Half Title
- Series Page
- Title Page
- Copyright Page
- Dedication
- Contents
- Preface
- Authors
- Acknowledgments
- I: Single-Input-Single-Output Neuronet
- II: Two-Input-Single-Output Neuronet
- III: Three-Input-Single-Output Neuronet
- IV: General Multi-Input Neuronet
- V: Population Applications Using Chebyshev-Activation Neuronet
- VI: Population Applications Using Power-Activation Neuronet
- VII: Other Applications
- Bibliography
- Glossary
- Index