Convolutional neural networks, or CNNs, are a type of deep learning model commonly used for image recognition and classification tasks. CNNs are inspired by the organization of the human visual cortex and are designed to automatically and adaptively learn spatial hierarchies of features from the input data. CNNs typically consist of multiple layers, including convolutional layers, pooling layers, and fully connected layers. Convolutional layers apply a series of filters to the input image to extract features, while pooling layers reduce the spatial dimensions of the features to make the network more computationally efficient. The fully connected layers then classify the extracted features into different categories. CNNs have been highly successful in various computer vision tasks, such as object recognition, facial recognition, and image segmentation. They have also been adapted for use in other domains, such as natural language processing and speech recognition. Over the years, CNNs have become one of the most popular and widely used deep learning models in the field of artificial intelligence and machine learning.