What Is a Multi-layer Perceptron(MLP)?
A multilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN). An MLP consists of at least three layers of nodes: an input layer , a hidden layer and an output layer .
Except for the input nodes, each node is a neuron that uses a nonlinear activation function . MLP utilizes a supervised learning technique called backpropagation for training.
ref:https://medium.com/@xzz201920/multi-layer-perceptron-mlp-4e5c020fd28a
A multilayer perceptron (MLP) is a deep, artificial neural network. They are composed of an input layer to receive the signal, an output layer that makes a decision or prediction about the input, and in between those two, an arbitrary number of hidden layers that are the true computational engine of the MLP.
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.