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What are Multilayer Perceptrons (MLPs) ?
A multilayer perceptron (MLP) is a fully connected class of feedforward artificial neural network (ANN) used in the field of deep learning. he term MLP is used ambiguously, sometimes loosely to mean any feedforward ANN, sometimes strictly to refer to networks composed of multiple layers of perceptrons. It is referred to as a “vanilla” neural network, especially when they have a single hidden layer. Multilayer Perceptrons has three layers : Input layer, hidden layer and Output layer. Hidden layer and output layer uses a nonlinear activation function. Backpropagation is a supervised learning technique used by MLP during training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron.
If all of the neurons in a multilayer perceptron have a linear activation function, that is, a linear function that maps the weighted inputs to each neuron's output, then linear algebra shows that any number of layers may be reduced to a two-layer input-output model. Some neurons use a nonlinear activation function in MLP that was created to model the frequency of biological neurons' action potentials.
Activation functions describe by :
In some deep learning models, the rectifier linear unit is commonly used as one of the possible ways to overcome the numerical problems related to the sigmoids.
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