Convolutional Neural Networks (CNNs): Image Recognition Explained

A Convolutional Neural Network (CNN) is a type of deep learning algorithm designed for image recognition and processing. It's a class of artificial neural network that uses a mathematical operation called convolution to extract features from input images. The idea behind CNNs is to mimic the way the human brain processes visual information by breaking down an image into smaller parts and analyzing each part separately.

CNNs consist of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The convolutional layers perform feature extraction by applying filters to the input image, while the pooling layers reduce the spatial dimensions of the feature maps. The fully connected layers are used for classification or regression tasks.

CNNs have been widely used in computer vision applications such as object recognition, face recognition, and image classification. They've shown impressive performance in many image analysis tasks and are considered a state-of-the-art technique for image processing.