What Is Convolutional Neural Network (CNN)?
A Convolutional Neural Network (CNN) is a deep learning architecture specifically designed for processing grid-like data such as images, using convolutional layers that apply learnable filters to detect spatial patterns like edges, textures, and objects.
How Convolutional Neural Network (CNN) Works
CNNs work by sliding small filters (kernels) across an image to detect local features. Early layers learn simple patterns like edges and colors, while deeper layers combine these into complex features like faces, cars, or text. Pooling layers reduce spatial dimensions, and fully connected layers make final predictions. CNNs revolutionized computer vision starting with AlexNet's breakthrough in 2012 and dominated image processing for a decade. While Vision Transformers have recently matched or exceeded CNN performance, CNNs remain widely used for their efficiency, especially on mobile and edge devices. They are also applied to audio spectrograms, time series, and other structured data.
Real-World Examples
A self-driving car using CNNs to detect pedestrians, traffic signs, and lane markings in real time
A medical imaging system using a CNN to detect tumors in MRI scans with high accuracy
A smartphone camera app using a lightweight CNN to identify plants and animals from photos