About FeaturesFeatures. Teaches the reader about what neural networks are, and how to manipulate them within the Mathematica environment. Shows how Mathematica can be used to implement and experiment with neural network architectures. Addresses a major topic related to neural networks in each chapter, or a specific type of neural network architecture. Contains exercises, suggested projects, and supplementary reading lists with each chapter. Includes Mathematica application programs ('packages') in Appendix. (Also available electronically from MathSource.).

Downloads (6 weeks) 0. Downloads (12 months) 0. Downloads (cumulative) 0. Simulating Neural Networks with Mathematica. From the Publisher: application, in the context of the interactive Mathematica environment.

Description. Copyright 1994. Dimensions: 6-3/8' x 9-1/4'. Pages: 352. Edition: 1st.Book. ISBN-10: 0-201-56629-X. ISBN-13: 978-0-201-56629-1This book introduces neural networks, their operation, and application, in the context of the interactive Mathematica environment.

Readers will learn how to simulate neural network operations using Mathematica, and will learn techniques for employing Mathematica to assess neural network behavior and performance. Hellhound metal fire from hell lyrics. For students of neural networks in upper-level undergraduate or beginning graduate courses in computer science, engineering, and related areas. Also for researchers and practitioners interested in using Mathematica as a research tool. Features. Teaches the reader about what neural networks are, and how to manipulate them within the Mathematica environment.

Shows how Mathematica can be used to implement and experiment with neural network architectures. Addresses a major topic related to neural networks in each chapter, or a specific type of neural network architecture. Contains exercises, suggested projects, and supplementary reading lists with each chapter. Includes Mathematica application programs ('packages') in Appendix. (Also available electronically from MathSource.)Table of ContentsIntroduction to Neural Networks and MathematicaTraining by Error MinimizationBackpropagation and Its VariantsProbability and Neural NetworksOptimization and Constraint Satisfaction with Neural NetworksFeedback and Recurrent NetworksAdaptive Resonance TheoryGenetic Algorithms020156629XB04062001.