Convolutional Neural Networks (CNNs) are inspired by how our visual cortex understands shapes, edges, and patterns. This blog explains CNNs with simple intuition, real experiments like the Hubel & Wiesel cat study, the evolution from the Neocognitron to modern deep learning models, and practical applications in computer vision.

The Perceptron is one of the simplest yet most important algorithms in supervised learning. Acting as the foundation for modern neural networks, it uses inputs, weights, and an activation function to make binary predictions. In this guide, we explore how the Perceptron learns, interprets weights, and forms decision boundaries — along with its biggest limitation: linear separability.