The backpropagation is an algorithm designed to compute the gradient of the cost function, and instead of using the simple step function, the method is about applying the sigmoid one. The method is a backward propagation of errors since its purpose is calculating “weight updates that minimize the error function from the output to the input layer.” Data is fed through the network from the inputs to the outputs, and then error values “feedback, making changes to the weights of nodes,” and the action is repeated until the error values are sufficiently small. The algorithm tends to be slow in solving real-world problems and is rather inefficient since solving a simple problem may require thousands of epochs.
Explain how the backpropagation algorithm works.
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