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JMP 10 Modeling and Multivariate Methods by SAS Institute

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Contents 
 
Overview of Neural Networks
Think of a neural network as a function of a set of derived inputs, called hidden nodes. The hidden nodes are nonlinear functions of the original inputs. You can specify up to two layers of hidden nodes, with each layer containing as many hidden nodes as you want.
Figure 10.2 shows a two-layer neural network with three X variables and one Y variable. In this example, the first layer has two nodes, and each node is a function of all three nodes in the second layer. The second layer has three ...

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