In this chapter, we have provided a broad overview of artificial neural networks, as well as a detailed examination of a few specific implementations. We began with a discussion of the basic properties of neural networks, training algorithms, and neural network architectures.

Next we provided an example of a simple static neural network implementing the XOR problem using Java. This example provided detailed explanation of the code used to build and train the network, including some of the math behind the weight adjustments during the training process. We then discussed dynamic neural networks and provided two in-depth examples, the MLP and SOM networks. These used the Weka tools to create and train the networks.

Finally, we concluded our chapter with a discussion of additional network architectures and algorithms. We chose some of the more popular networks to summarize and explored situations where each type would be most useful. We also included a discussion of backpropagation in this section.

In the next chapter, we will expand upon this introduction and take a look at deep learning with neural networks.