Artificial Neural Networks

Overview

Imagine for a moment the vast and intricate web of thoughts, emotions, and decisions swirling in your mind. Each time you recognize a face, solve a problem, or recall a memory, your brain works through a complex network of interconnected neurons. These neurons communicate through electrical signals and chemical neurotransmitters, creating a dynamic and adaptable system that enables you to think, learn, and react in remarkable ways.

What if we could capture some of this extraordinary capability and translate it into computer technology? This is the essence of artificial neural networks (ANNs). Inspired by the brain’s neural networks, ANNs mimic how our brains process information, allowing computers to recognize patterns, learn from data, and make decisions with increasing accuracy. By emulating the brain’s ability to adapt and learn, ANNs represent a leap toward creating intelligent systems that can think and learn in ways similar to human cognition.

How do ANNs work?

An ANN’s heart is a network of nodes or neurons organized into layers: an input layer, one or more hidden layers, and an output layer. Each neuron in one layer connects to neurons in the next layer, and these connections are weighted to influence how the network processes input data. During training, the network adjusts these weights to improve its predictions or classifications, like how our brain strengthens or weakens connections based on learning and experience. Each neuron applies an activation function to the input it receives. This function introduces non-linearity into the network, allowing it to learn complex patterns.

A single neuron in an artificial neural network processes input values through a weighted sum followed by an activation function. Mathematically, it computes the weighted sum $z_j$​ of its inputs $x_i$​ with associated weights $w_{ij}​$ and adds a bias $b_j$​, given by:

 $z_j=∑_{i=1}^n w_{ij} x_i +b_j$​. 

This weighted sum is then passed through an activation function $ϕ$ to produce the neuron’s output $a_j$​, calculated as $a_j=ϕ(z_j)$ ...