When the input data is transmitted into the neuron, it is processed, and an output is generated.Here is a diagram that shows the structure of a simple neural network:And, the best way to understand how neural networks work is to learn how to build one from scratch (without using any library).In this article, we’ll demonstrate how to use the Python programming language to create a simple neural network.Here is a table that shows the problem.We are going to train the neural network such that it can predict the correct output value when provided with a new set of data.As you can see on the table, the value of the output is always equal to the first value in the input section..The class will also have other helper functions.Even though we’ll not use a neural network library for this simple neural network example, we’ll import the numpy library to assist with the calculations.The library comes with the following four important methods:We’ll use the Sigmoid function, which draws a characteristic “S”-shaped curve, as an activation function to the neural network.This function can map any value to a value from 0 to 1..For example, if the output variable is “x”, then its derivative will be x * (1-x).This is the stage where we’ll teach the neural network to make an accurate prediction..Every input will have a weight—either positive or negative.This implies that an input having a big number of positive weight or a big number of negative weight will influence the resulting output more.Remember that we initially began by allocating every weight to a random number.Here is the procedure for the training process we used in this neural network example problem:We used the “.T” function for transposing the matrix from horizontal position to vertical position.. More details