How does a convolutional neural network (CNN) work?

A Convolutional Neural Network (CNN) is a type of deep learning algorithm used primarily for image classification and recognition tasks. Its success lies in its ability to automatically learn hierarchical patterns from data.

CNN Layers:

A typical CNN consists of three main types of layers - convolutional, pooling, and fully connected layers. The convolutional layers extract features from input images using filters. The pooling layers down-sample the features to reduce computational complexity. The fully connected layers perform the classification based on the extracted features.

Convolution and Feature Learning:

The convolution process involves applying filters to the input image to extract features such as edges, textures, and shapes. As the network progresses through multiple convolutional layers, it learns increasingly complex features, enabling it to distinguish between different objects in the images.

Pooling and Activations:

Pooling layers reduce the spatial dimensions of the features, making the network more robust to variations in input. Activations like ReLU introduce non-linearity, allowing the network to learn complex patterns and relationships in the data.

Classification:

Once the features are learned, the fully connected layers combine them to make the final classification decision. The network assigns probabilities to each possible class, and the class with the highest probability is selected as the output.