MACHINE LEARNING

Detailed explanation-1: -Perceptron is a function that maps its input “x, ‘’ which is multiplied with the learned weight coefficient; an output value ‘’f(x)”is generated. In the equation given above: “w” = vector of real-valued weights. “b” = bias (an element that adjusts the boundary away from origin without any dependence on the input value)

Detailed explanation-2: -In the context of neural networks, a perceptron is an artificial neuron using the Heaviside step function as the activation function. 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.

Detailed explanation-3: -Perceptron Weighted Sum The first step in the perceptron classification process is calculating the weighted sum of the perceptron’s inputs and weights. To do this, multiply each input value by its respective weight and then add all of these products together.