An artificial perceptron is the fundamental computational building block of artificial neural networks, executing linear binary classification. Multiple numerical inputs are modulated by learnable synaptic connection weights, aggregated with a scalar bias offset, and projected through a nonlinear activation function like the Sigmoid or ReLU. By adjusting weights via gradient-based backpropagation, the perceptron synthesizes hyperplanes separating multidimensional data classes.