An Artificial Neural Network consists of multiple layers of nodes, with each layer fully connected to the next. To specify an MLP it is necessary to define: the activation function of the neurons, the number of hidden layers and the number of nodes per layer.
In other words, a multilayer perceptron (MLP)is a supervised learning algorithm that learns a function by training on a data set. Given a set of features and a target, it can learn a nonlinear function approximator (= approximation) for classification or regression. It differs from logistic regression in that there may be one or more nonlinear layers called hidden layers between the input and output layers. The figure shows an MLP with a hidden layer with scalar output.
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