How does a convolutional neural network (CNN) work?

A convolutional neural network (CNN) is a type of deep learning model designed to process grid-like data, such as images, by automatically learning hierarchical features through layers of mathematical operations. At its core, a CNN applies convolutional filters to input data to detect spatial patterns, followed by downsampling and nonlinear transformations. These steps enable the network to progressively identify complex structures—from edges and textures in early layers to object parts and full objects in deeper layers.

The architecture of a CNN typically includes three key components: convolutional layers, pooling layers, and fully connected layers. Convolutional layers use small filter matrices (e.g., 3x3 or 5x5 pixels) that slide across the input, computing dot products to produce feature maps. For example, a filter might detect horizontal edges in an image by emphasizing pixel intensity changes. Pooling layers, such as max-pooling, reduce the spatial dimensions of feature maps by retaining the maximum value in local regions (e.g., 2x2 windows), which helps control computational complexity and improves translation invariance. Fully connected layers at the end of the network map the learned features to output classes (e.g., classifying an image as “cat” or “dog”). During training, backpropagation adjusts the filter weights to minimize prediction errors.

A practical example is training a CNN to recognize handwritten digits. The first convolutional layer might learn to detect strokes, while deeper layers combine these into digit-specific shapes. ReLU activation functions introduce nonlinearity after each convolution, allowing the network to model complex relationships. Dropout layers may be added to prevent overfitting. Developers often use frameworks like TensorFlow or PyTorch to implement CNNs, defining layer configurations (e.g., filter counts, stride lengths) and optimizing hyperparameters like learning rate. This structured approach enables CNNs to scale efficiently for tasks like medical image analysis or autonomous driving, where local patterns and spatial hierarchies are critical.