Data Science #34 - The deep learning original paper review, Hinton, Rumelhard & Williams (1985)

On the 34th episode, we review the 1986 paper, "Learning representations by back-propagating errors" , which was pivotal because it provided a clear, generalized framework for training neural networks with internal 'hidden' units. The core of the procedure, back-propagation, repeatedly adjusts the weights of connections in the network to minimize the error between the actual and desired output vectors. Crucially, this process forces the hidden units, whose desired states aren't specified, to develop distributed internal representations of the task domain's important features.This capability…