Perceptron: Is a commonly used term in

Perceptron is a commonly used term in the arena of Machine Learning and Artificial Intelligence. Being the most basic component of Machine Learning and Deep Learning technologies, the perceptron is the elementary unit of an Artificial Neural Network. In this article, you will learn what is perceptron and compare perceptron vs neuron to understand how it is similar to the neurons in our brain. Breaking down the perceptron further, we will dive into its components, perceptron learning rule ... The perceptron algorithm is also termed the single-layer perceptron, to distinguish it from a multilayer perceptron, which is a misnomer for a more complicated neural network. A Perceptron is an algorithm used for supervised learning of binary classifiers. Binary classifiers decide whether an input, usually represented by a series of vectors, belongs to a specific class. In short, a perceptron is a single-layer neural network consisting of four main parts including input values, weights and bias, net sum, and an activation function. A ‘Perceptron’ is the basic building block, or single node, of a neural network inspired from the neurons that are found in the brain. It operates by taking in a set of inputs, calculating a weighted sum, adding a bias term, and then applying an activation function to this sum to produce an output.