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How A Multi-Layer Neural Network Works

  • The inputs to the network correspond to the attributes measured for each training tuple
  • Inputs are fed simultaneously into the units making up the input layer
  • They are then weighted and fed simultaneously to a hidden layer
  • The number of hidden layers is arbitrary, although usually only one
  • The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network's prediction
  • The network is feed-forward: None of the weights cycles back to an input unit or to an output unit of a previous layer
  • From a statistical point of view, networks perform nonlinear regression: Given enough hidden units and enough training samples, they can closely approximate any function

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