Programming Neural Networks with Encog 2 in Java

Beginning where our introductory neural network programing book left off, this book introduces you to Encog. Encog allows you to focus less on the actual implementation of neural networks and focus on how to use them. Encog is an advanced neural network programming framework that allows you to create a variety of neural network architectures using the Java programming language. Neural network architectures such as feedforward/perceptrons, Hopfield, Elman, Jordan, Radial Basis Function, and Self Organizing maps are all demonstrated. This book also shows how to use Encog to train neural networks using a variety of means. Several propagation techniques, such as back propagation, resilient propagation (RPROP) and the Manhattan update rule are discussed. Additionally, training with a genetic algorithm and simulated annealing is discussed as well. You will also see how to enhance training using techniques such as pruning and hybrid training.

Table of Contents

Chapter 1: Introduction to Encog
Chapter 2: Building Encog Neural Networks
Chapter 3: Using Activation Functions
Chapter 4: Using the Encog Workbench
Chapter 5: Propagation Training
Chapter 6: Obtaining Data for Encog
Chapter 7: Encog Persistence
Chapter 8: More Supervised Training
Chapter 9: Unsupervised Training Methods
Chapter 10: Using Temporal Data
Chapter 11: Using Image Data
Chapter 12: Recurrent Neural Networks
Chapter 13: Structuring Hidden Layers
Chapter 14: Other Network Patterns
Appendix A: Installing and Using Encog
Appendix B: Example Locations
Appendix C: Encog Patterns