We describe a new learning procedure, back-propagation, for networks of neurone-like
units. The procedure repeatedly adjusts the weights of the connections in the network so as
to minimize a measure of the difference between the actual output vector of the net and the
desired output vector. As a result of the weight adjustments, internal 'hidden'units which are
not part of the input or output come to represent important features of the task domain, and ...