Neural Networks in R: From Biological Inspiration to Real-World Intelligence

Artificial Intelligence has transformed the way organizations analyse data and make decisions. Among the many techniques in AI, neural networks stand out as one of the most powerful and flexible modelling approaches. Inspired by the human nervous system, neural networks are capable of detecting complex patterns, handling non-linear relationships, and generating highly accurate predictions. In this article, we will explore the origins of neural networks, understand how they work, implement and visualize them in R, and examine real-world use cases and case studies across industries. The Origins of Neural Networks The concept of neural networks dates back to the 1940s when researchers first attempted to mathematically model how biological neurons function. Early work by Warren McCulloch and Walter Pitts introduced a simplified computational model of a neuron. Later, Frank Rosenblatt developed the perceptron, one of the earliest neural network models. The perceptron was a single-layer neur