Introduction to Neural Networks with Java

Introduction to Neural Networks with Java

“Introduction to Neural Networks with Java, Second Edition”, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence.


Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated annealing are also introduced. Practical examples are given for each neural network. Examples include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots. All Java source code is available online for easy downloading.

Table of Contents

  • Overview of Neural Networks
  • Matrix Operations
  • Using a Hopfield Neural Network
  • How a Machine Learns
  • Feedforward Backpropagation Neural Networks
  • Understanding Genetic Algorithms
  • Understanding Simulated Annealing
  • Pruning Neural Networks
  • Predictive Neural Networks
  • Application to the Financial Markets
  • Understanding the Self-Organizing Map
  • OCR with the Self-Organizing Map
  • Bot Programming and Neural Networks
  • The Future of Neural Networks

Book Details

Author(s): Jeff Heaton
Format(s): HTML
Number of pages: 440
Link: Read online.

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