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OverviewThis book introduces neural networks, their operation and their application, in the context of Mathematica, a mathematical programming language. Readers will learn how to simulate neural network operations using Mathematica and will learn techniques for employing Mathematics to assess neural network behaviour and performance. It shows how this popular and widely available software con be used to explore neural network technology, experiment with various architectures, debug new training algorithms and design techniques for analyzing network performance. Features: Addresses a major neural network topic or a specific network architecture in each chapter includes an introduction to genetic a/gorithms Vlncludes Mathematica listings in an appendix. Full Product DetailsAuthor: James FreemanPublisher: Pearson Education (US) Imprint: Addison-Wesley Educational Publishers Inc Edition: 1st Revised edition Dimensions: Width: 16.70cm , Height: 1.90cm , Length: 16.70cm Weight: 0.490kg ISBN: 9780201566291ISBN 10: 020156629 Pages: 352 Publication Date: 01 July 2020 Audience: College/higher education , Tertiary & Higher Education Format: Paperback Publisher's Status: Out of Print Availability: In Print  Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock. Table of ContentsIntroduction to Neural Networks and Mathematica. Training by Error Minimization. Backpropagation and Its Variants. Probability and Neural Networks. Optimization and Constraint Satisfaction with Neural Networks. Feedback and Recurrent Networks. Adaptive Resonance Theory. Genetic Algorithms. 020156629XT04062001ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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