Convolution is the core operation behind Convolutional Neural Networks (CNNs) that enables machines to understand images. This blog explains convolution from first principles, starting with how images are represented in memory and progressing to edge detection, feature maps, RGB convolution, and the role of multiple filters. Through intuitive explanations and practical examples, you will gain a clear understanding of how CNNs extract hierarchical features from images.