Backpropagation Network using Python
Backpropagation neural network is used to improve the accuracy of neural network and make them capable of self-learning. Backpropagation means “backward propagation of errors”. Here error is spread into the reverse direction in order to achieve better performance.
Backpropagation is an algorithm for supervised learning of artificial neural networks that uses the gradient descent method to minimize the cost function. It searches for optimal weights that optimize the mean-squared distance between the predicted and actual labels.
What is Backpropagation Neural Network(BPN)?
BPN was discovered by Rumelhart, Williams & Honton in 1986. The core concept of BPN is to backpropagate or spread the error from units of output layer to internal hidden layers in order to tune the weights to ensure lower error rates. It is considered a practice of fine-tuning the weights of neural networks in each iteration. Proper tuning of the weights will make a sure minimum loss and this will make a more robust, and generalizable trained neural network.
