One Layer Neural Network From Scratch — Classification

Classification is a supervised learning method. In supervised learning, we have labels for our data. Algorithms learn from these labels and make predictions accordingly. Classification aims to divide the dataset into classes. For example, let’s consider data of people applying for credit. Deciding whom to grant credit or not based on past granted/not granted credits is called binary classification.

In this article, we will build a single-layer artificial neural network from scratch for binary classification. But before that, we need to understand what artificial neural networks are and the mathematics behind them

Artificial Neural Networks

Artificial Neural Networks are an artificial intelligence method inspired by the human brain. They consist of interconnected neurons and layers, similar to the human brain. Each neuron performs specific mathematical operations, and we reach the final result in the output layer. They are very beneficial for problems involving non-linear and complex inputs and outputs.

Neural Network Structure for Classification Model

The input layer takes two inputs, X1 and X2. Bias (b) and Weights (W1, W2) are parameters and get updated during the training phase. The equation for z…