How A Multi-Layer Neural Network Works?

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  • 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 in that 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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