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Backpropagation Algorithm Math . The goal of backpropagation is to optimize the weights so that the neural network can learn how to correctly map arbitrary inputs to outputs. The backpropagation algorithm looks for the minimum value of the error function in weight space using a technique called the delta rule or. Neural Networks The Backpropagation algorithm in a from www.datasciencecentral.com For the rest of this tutorial we’re going to work with a single training set: The training algorithm of backpropagation involves four stages which are as follows − initialization of weights − there are some small random values are assigned. “the backpropagation algorithm was originally introduced in the 1970s, but its importance wasn’t fully appreciated until a famous 1986 paper by david rumelhart, geoffrey hinton, and ronald williams”.