Perceptron

Perceptron: Definition: It’s a step function based on a linear combination of real-valued inputs. If the combination is above a threshold it outputs a 1, otherwise it outputs a –1.

A perceptron draws a hyperplane as the decision boundary over the (n-dimensional) input space.

A perceptron can learn only examples that are called “linearly separable”. These are examples that can be perfectly separated by a hyperplane.

Perceptrons can learn many boolean functions: AND, OR, NAND, NOR, but not XOR
However, every boolean function can be represented with a perceptron network that has two levels of depth or more.
The weights of a perceptron implementing the AND function is shown below.