Simulating Neural Networks with Mathematica by James Freeman

Simulating Neural Networks with Mathematica by James Freeman

Regular price
Checking stock...
Regular price
Checking stock...
World of Books

At World of Books, you’ll find millions of preloved reads at great prices, from bestsellers to hidden gems. Every book you buy saves money and helps reduce waste, so you can read more for less while giving stories a second life.

The feel-good place to buy books
  • Free US shipping over $15
  • Buying preloved emits 41% less CO2 than new
  • Millions of affordable books
  • Give your books a new home - sell them back to us!

Simulating Neural Networks with Mathematica by James Freeman

This 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. 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 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 020156629XB04062001
Freeman, James: - James Freeman is assistant editor of The Wall Street Journal's editorial page and author of the Best of the Web column. He is coauthor of Borrowed Time: Two Centuries of Booms, Busts, and Bailouts at Citi, a New York Times Editors' Choice and Financial Times Business Book of the Month. He is a Fox News contributor and former investor advocate at the US Securities and Exchange Commission.
SKU Unavailable
ISBN 13 9780201566291
ISBN 10 020156629X
Title Simulating Neural Networks with Mathematica
Author James Freeman
Condition Unavailable
Binding Type Paperback
Publisher Pearson Education (US)
Year published 2020-07-01
Number of pages 352
Cover note Book picture is for illustrative purposes only, actual binding, cover or edition may vary.