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Java for Data Science by Jennifer L. Reese, Richard M. Reese

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Understanding dynamic neural networks

Dynamic neural networks differ from static networks in that they continue learning after the training phase. They can make adjustments to their structure independently of external modification. A feedforward neural network (FNN) is one of the earliest and simplest dynamic neural networks. This type of network, as its name implies, only feeds information forward and does not form any cycles. This type of network formed the foundation for much of the later work in dynamic ANNs. We will show in-depth examples of two types of dynamic networks in this section, MLP networks and SOMs.

Multilayer perceptron networks

A MLP network is a FNN with multiple layers. The network uses supervised learning with backpropagation ...

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