Multi-Layer Perceptrons (MLPs) are a fundamental type of artificial neural network used in deep learning, designed to model complex relationships in data. An MLP consists of multiple layers of nodes: an input layer, one or more hidden layers, and an output layer. Each node, or neuron, in a layer is connected to every neuron in the next layer, forming a densely connected network. The neurons in the hidden layers apply non-linear activation functions to transform the input data, allowing the network to learn and represent complex patterns.

MLPs are particularly effective for tasks like classification, regression, and even more sophisticated problems such as image recognition and natural language processing when appropriately scaled. By adjusting the weights of the connections between neurons through a process known as backpropagation, the MLP learns from data and minimizes errors in its predictions. As one of the earliest and most straightforward forms of deep learning models, MLPs serve as the building blocks for more advanced neural network architectures, making them essential tools in the field of machine learning.