A Multi-Layer Perceptron (MLP) is a class of feedforward artificial neural network that consists of multiple layers of nodes or neurons. Each layer in an MLP is fully connected to the next layer, which means that every neuron in one layer is connected to every neuron in the following layer. The MLP is composed of an input layer, one or more hidden layers, and an output layer.
The key characteristic of MLPs is their ability to learn non-linear functions through the use of activation functions applied to the outputs of neurons. Common activation functions include the sigmoid, tanh, and ReLU (Rectified Linear Unit). These functions introduce non-linearity into the model, enabling the MLP to capture complex patterns in the data.
During the training process, MLPs use a supervised learning technique called backpropagation. In this method, the network adjusts its weights based on the error rate obtained in the previous epoch (iteration). The goal is to minimize this error across the training dataset. MLPs are particularly effective for tasks such as classification, regression, and pattern recognition.
Despite their flexibility and power, MLPs can be prone to overfitting, especially when the model is too complex relative to the amount of available training data. Techniques such as dropout, regularization, and early stopping are often employed to mitigate this risk. Overall, Multi-Layer Perceptrons serve as a foundational concept in the field of deep learning and are widely used in various applications across industries.