Backpropagation in Neural Network is a supervised learning algorithm, for training Multi-layer Perceptrons (Artificial Neural Networks).
The Backpropagation algorithm in neural network looks for the minimum value of the error function in weight space using a technique called the delta rule or gradient descent. The weights that minimize the error function is then considered to be a solution to the learning problem.
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