Convolutional Neural Networks (CNNs) are a class of deep learning algorithms primarily used for processing structured grid data like images. They are designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers.

The convolution layers apply a set of filters to the input image, where each filter scans the image in small sections to detect features such as edges, textures, and patterns. These filters, also known as kernels, slide over the input data, performing element-wise multiplications and summing the results to produce a feature map. This process allows CNNs to preserve the spatial relationships in the data, making them highly effective for image recognition tasks.

Pooling layers, another essential component, reduce the dimensionality of each feature map but retain the most critical information. This is achieved through operations like max pooling, where the maximum value in each patch of the feature map is selected, which helps in reducing the computational load and mitigating overfitting.

Finally, the fully connected layers act like traditional neural networks, where each neuron is connected to all neurons in the previous layer. These layers integrate the features detected in the convolutional and pooling layers and combine them to make the final prediction.

CNNs are widely used in applications such as image and video recognition, medical image analysis, and even natural language processing tasks. Their ability to automatically detect features and patterns in images makes them indispensable for tasks involving visual data.