MACHINE LEARNING

Detailed explanation-1: -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.

Detailed explanation-2: -In other words, there are two single layer perceptron networks. Each perceptron produces a line. Knowing that there are just two lines required to represent the decision boundary tells us that the first hidden layer will have two hidden neurons.

Detailed explanation-3: -The MLP is a feedforward neural network with an input layer of source neurons, at least one hidden layer of computational neurons, and an output layer of computational neurons. The input layer accepts input signals from the outside world and redistributes these signals to all neurons in the hidden layer.