A. Perceptrons - charlsefrancis/neural-networks GitHub Wiki

A. Perceptron [Perceptron]click to see image

Perceptron model, proposed by Minsky-Papert is one of the simplest and oldest models of Neuron. It is the smallest unit of neural network that does certain computations to detect features or business intelligence in the input data. It accepts weighted inputs, and apply the activation function to obtain the output as the final result. Perceptron is also known as TLU(threshold logic unit)

Perceptron is a supervised learning algorithm that classifies the data into two categories, thus it is a binary classifier. A perceptron separates the input space into two categories by a hyperplane represented by the following equation [Perceptron equation]click to see image

Advantages of Perceptron: Perceptrons can implement Logic Gates like AND, OR, or NAND

Disadvantages of Perceptron Perceptrons can only learn linearly separable problems such as boolean AND problem. For non-linear problems such as boolean XOR problem, it does not work.