Backpropagation in Neural Networks

Do you know how a neural network trains itself to do some job? In this article, we will see the whole process of how a neural network learns. The main goal of a network is to reduce the loss incurring while predicting the outputs. To minimize this loss, we will apply some optimization technique called Gradient descent. In this technique, we update the value of parameters while backpropagating in the network, i.e., find the derivates of the error function with respect to the weights to decrease the loss function and use this Gradient to update the current weight.