Convolutional Neural Networks (CNNs) are a type of deep learning model primarily used for analyzing visual data, such as images and videos. The architecture of a CNN is inspired by the human visual system and consists of several key layers designed to automatically and adaptively learn spatial hierarchies of features. A typical CNN architecture includes a series of convolutional layers, pooling layers, and fully connected layers.

The convolutional layers are the core building blocks of a CNN, where a set of filters is applied to the input data to extract features such as edges, textures, and shapes. These filters slide over the input data, capturing local patterns that are then combined to form higher-level features in subsequent layers. Pooling layers follow the convolutional layers and are used to reduce the spatial dimensions of the data, which helps in reducing the computational complexity and prevents overfitting by making the model more invariant to small translations or distortions in the input.

Finally, the fully connected layers, often found at the end of the CNN, take the high-level feature representations learned from the previous layers and use them to perform the final classification or regression task. The output layer generates the final prediction, usually in the form of class probabilities. This hierarchical structure allows CNNs to automatically learn complex patterns in the data, making them highly effective for image recognition, object detection, and other tasks involving visual inputs.