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Perceptron is a machine learning algorithm that helps provide classified outcomes for computing. It dates back to the 1950s and represents a fundamental example of how machine learning algorithms work to develop data.
Experts call the perceptron algorithm a supervised classification because the computer is aided by the human classification of data points. Perceptron is also related to the development of "artificial neural networks," where computing structures are based on the design of the human brain.
In perceptron, the algorithm takes a set of inputs and returns a set of outputs. These are often presented visually in charts for users. In many computer programming languages, a perceptron algorithm can take the form of a "for" or a "while" loop, where each input is processed to produce an output. The results show how these advanced types of algorithms learn from data — one of the defining characteristics of perceptron is that it is not just an iterative set of processes, but an evolving process where the machine learns from data intake over time.