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  • Iteratively process a set of training tuples & compare the network's prediction with the actual known target value
  • For each training tuple, the weights are modified to minimize the mean squared error between the network's prediction and the actual target value
  • Modifications are made in the “backwards” direction: from the output layer, through each hidden layer down to the first hidden layer, hence “backpropagation
  • Steps
    • Initialize weights to small random numbers, associated with biases
    • Propagate the inputs forward (by applying activation function)
    • Backpropagate the error (by updating weights and biases)
    • Terminating condition (when error is very small, etc.)

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