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For question about Multi Layer Perceptron model/architecture, its training and other related details and parameters associated with the model.

A multi-layer perceptron (MLP) is a class of feed-forward artificial neural network. An MLP consists of at least three layers of nodes. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called back-propagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.

Source: Wikipedia - Multilayer perceptron