Multilayer perceptrons are information processing systems based on artificial neural network models. They are designed to perform complex tasks such as data classification or pattern recognition. Unlike other types of neural networks, multilayer perceptrons require a labeled dataset to train effectively. They are inspired by the Rosenblatt perceptron concept but are distinguished by the presence of at least one intermediate, or “hidden,” layer of neurons.