Coding Club Of Competitive Programmers

Objective: Application of Multi-Layer Perceptron on classification Problem Theory: Multi-layer perceptron (MLP) is a supplement of a feed-forward neural network. It consists of three types of layers—the input layer, output layer, and hidden layer, as shown in Fig. below. The input layer receives the input signal to be processed. The required task such as prediction and classification is performed by the output layer. An arbitrary number of hidden layers that are placed in between the input and output layer are the true computational engine of the MLP. Similar to a feed-forward network in an MLP the data flows in the forward direction from input to output layer. The neurons in the MLP are trained with the backpropagation learning algorithm. MLPs are designed to approximate any continuous function and can solve problems that are not linearly separable. The major use cases of MLP are pattern classification, recognition, prediction, and approximation. The computations taking place at every...