Convolutional Neural Networks work similarly to photo filters on your smartphone. Just as Instagram filters scan across your photos to create artistic effects, CNN filters scan across images to detect important features like edges, corners, and patterns. This scanning process is called convolution, and it's the foundation of how computers learn to see and understand images.
The convolution layer is the core of CNN architecture. It applies multiple filters to the input image, with each filter designed to detect specific features like edges, corners, or textures. As the filter slides across the image, it performs element-wise multiplication and summation to create feature maps. These feature maps highlight where particular patterns are detected in the original image.