CNN Theory

Convolutional Neural Networks (CNNs) are a type of machine learning model designed to recognize patterns in visual data, like images. They work by applying filters that detect basic features such as edges, textures, and shapes across the image. These features are then combined and summarized through layers, allowing the system to identify more complex patterns, objects, or scenes. CNNs learn these filters automatically during training, improving their accuracy over time. They are widely used in image recognition, object detection, and related tasks because of their ability to efficiently process spatial hierarchies in visual data.