Simulating Neural Networks with Mathematica
Author:   James Freeman
Publisher:   Pearson Education (US)
Edition:   1st Revised edition
ISBN:  

9780201566291


Pages:   352
Publication Date:   01 July 2020
Format:   Paperback
Availability:   In Print   Availability explained
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

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Simulating Neural Networks with Mathematica


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Overview

This 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 Details

Author:   James Freeman
Publisher:   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:  

9780201566291


ISBN 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   Availability explained
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of Contents

Introduction 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. 020156629XT04062001

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