Perceptron

A perceptron is a type of artificial neuron that serves as a fundamental building block for neural networks. It takes multiple binary inputs, applies weights to them, and produces a single binary output based on a threshold function. The perceptron is characterized by its simplicity and is often used in supervised learning tasks. Common use cases include binary classification problems, where it can distinguish between two classes based on input features. Despite its limitations, such as the inability to solve non-linear problems, the perceptron laid the groundwork for more complex models in deep learning.