Back-Propagation
	• Basic idea: Supply training inputs, computation feeds forward, error computed with training output, error propagates backward for weight updates. – Start with final layer – 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