A Convolutional Neural Network (CNN) is a specialized type of artificial neural network designed to process and analyze visual data, particularly images. Think of it as a sophisticated pattern recognition system that learns to identify features in images much like the human visual system. At its core, a CNN processes images by breaking them down into smaller pieces, analyzing each piece for specific patterns, and then combining this information to understand the entire image.
Convolutional Neural Network Architecture
The term "convolution" refers to the main mathematical operation that gives these networks their power. A convolution works by sliding a small window (called a filter or kernel) across an image and performing calculations at each position. These filters act like feature detectors - some might detect edges, others might detect textures or specific shapes. As the network processes more images during training, it automatically learns which features are most important for its specific task, whether that's recognizing faces, identifying objects, or detecting medical conditions in X-rays.
Computer Vision CNN
In a typical CNN, several types of layers work together to process information. The convolutional layers perform the feature detection described above. Pooling layers help reduce the size of the processed information while maintaining important features. Finally, fully connected layers combine all this information to make the final decision about what's in the image. For example, in a CNN designed to recognize handwritten digits, early layers might detect simple edges and curves, middle layers might combine these into recognizable parts of numbers, and final layers would use this information to decide which digit (0-9) is present in the image.
