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The backpropagation algorithm was originally introduced in the 1970s, but its importance wasn’t fully appreciated until a famous paper in 1986 by David Rumelhart, Geoffrey Hinton, and Ronald Williams. The paper describes several neural networks where backpropagation works far faster than earlier approaches to learning, making it possible to use neural nets to solve problems which had previously been insoluble. Today, the backpropagation algorithm is the workhorse of learning in neural networks. Although Backpropagation is the widely used and most successful algorithm for the training of a neural network of all time, there are several factors which affect the Error-Backpropagation training algorithms.