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JMP 11 Specialized Models by SAS Institute

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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 4.2 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 nodes, and all nodes are a function of the three X variables. The predicted Y variable is a function of both nodes in the first layer.
Figure 4.2 Neural Network Diagram
The functions applied at the nodes of the hidden layers are ...

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