Convolutional networks, also known as Convolutional Neural Networks (CNNs), are a type of deep learning algorithm commonly used in image recognition, object detection, and other computer vision tasks. They are designed to automatically and adaptively learn hierarchical patterns and features from digital images by using convolutional layers to filter and process the input data. The fundamental building block of convolutional networks is the convolutional layer, which applies a series of filters or kernels to the input data to extract important features. These filters are typically small in size and are slid across the input data to capture spatial relationships and patterns. Convolutional networks also often include pooling layers to downsample and reduce the dimensionality of the feature maps produced by the convolutional layers. Overall, convolutional networks have revolutionized the field of computer vision and have achieved state-of-the-art performance on a wide range of tasks, including image classification, object detection, and image segmentation. They have also been successfully adapted for use in other domains, such as natural language processing and speech recognition.