what is backpropagation in neural network

       what is backpropagation in neural network

Backpropagation (backward propagation) is an important mathematical tool for improving the accuracy of predictions in data mining and machine learning. ... Artificial neural networks use backpropagation as a learning algorithm to compute a gradient descent with respect to weights

• Backpropagation is a neural network learning algorithm.

 • A neural network is a set of connected input/output units in which each connection has a weight associated with it.

During the learning phase, the network learns by adjusting the weights so as to be able to predict the correct class label of the input tuples.

Neural network learning is also referred to as connectionist learning due to the connections between units.

Purpose of Backpropagation

Backpropagation, short for "backward propagation of errors," is an algorithm for supervised learning of artificial neural networks using gradient descent. Given an artificial neural network and an error function, the method calculates the gradient of the error function with respect to the neural network's weights.