Convolutional neural networks (CNNs) are a type of artificial neural network used in deep learning. They are specifically designed to process data that has a grid-like topology, such as images, so they are often used for image classification and other computer vision tasks. CNNs work by applying multiple filters to an input image, each extracting a different feature from the original image. The outputs from these filters can then be combined in various ways to make predictions about the content of the image. CNNs have been widely used for many applications in recent years due to their potential for high accuracy at relatively low computational cost.  

In addition to being used for image processing tasks, convolutional neural networks have also been applied in natural language processing and document classification. This is because they are able to model relationships between words in a text, as well as detect patterns in the data. Furthermore, CNNs can also be used for medical diagnosis and drug discovery, since they are capable of recognizing subtle patterns in large datasets. The use of convolutional neural networks is expected to expand further in the near future, with their potential applications continuing to grow.

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