A Convolutional Neural Network (CNN) is a type of deep learning algorithm specifically designed for analyzing visual data. It is a subset of machine learning and is particularly effective at identifying patterns in images to recognize objects, classes, and categories. CNNs are composed of multiple layers, including convolutional, rectified linear unit (ReLU), and pooling layers, which enable them to learn directly from the data and extract features from the input images. They are widely used in various applications such as image recognition, computer vision, and signal data classification, and are known for their computational efficiency and ability to run on different devices, including smartphones
. CNNs have become the go-to network architecture for tasks involving object recognition, image classification, and computer vision due to their ability to automatically learn and optimize filters through training, which was traditionally done through manual and time-consuming feature extraction methods. This independence from prior knowledge and human intervention in feature extraction is a major advantage of CNNs
