•Basic idea: Supply training inputs, computation feeds forward, error computed with training output, error propagates backward for weight updates.
–Update output weights of layer according to layer output error as with perceptron learning rule
–Assign error to units of previous layer according to weights
–Repeat this process backwards through layers
