The perceptron Single node perceptron Perceptrons form the basis for ANNs. Perceptron takes input and produces output as below: Input ➡️ Activation Function ➡️ Output Input If the weight is 0- input remains unaltered coming into the perceptron. Below is what happens to the input ∑ w i z i ≽ t then y = 1. Else y = 0. i t is the threshold which is set by the outgoing part. So the key to output is based on the weighted sum and the threshold. Activation function This is the processing part of the neuron and this determines output. So most commonly the ones used are the Sigmoid function and the hyperbolic tangent. QUESTION: Which activation function should I use? I am going to talk of three key activation functions a) Sigmoid The Sigmoid returns 0 or 1 and in code can be written as return 1.0/(1.0+Math.exp(-x) A Sigmoid is a mathematical curve having the characteristic S shape. DISADVANTAGE: Descending Gradient b) Tanh DISADVANTAGE: