Typically, a neural network is initially trained or fed large amounts of data. Training consists of providing input and telling the network what the output should be defining the rules and making determinations -- that is, each node decides what to send on to the next tier based on its own inputs from the previous tier -- neural networks use several principles. These include gradient-based training, fuzzy logic, genetic algorithms and Bayesian methods. They may be given some basic rules about object relationships in the space being modeled.
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