Convolutional Neural Networks

Convolutional neural networks (CNN), also known as ConvNets, are a type of deep learning model that is particularly effective for analyzing visual imagery. They are designed to automatically and adaptively learn spatial hierarchies of features from input data, such as images, through a specialized architecture that includes convolutional layers. These layers use filters (or kernels) to extract features from the input, including edges, textures, and shapes. The filters are learned during training and adapt to the specific task for which the CNN is designed. The pooling layers of CNNs reduce the spatial dimensions of the feature maps by downsampling the data. The fully connected layers at the end of the CNNs take the output from the convolutional and pooling layers and make the final classification or prediction. Salient characteristics of a convolutional neural network are described. The architecture of a CNN is discussed, and the functions of different layers are explained. The steps of training a CNN algorithm are elaborated, followed by the use of a trained CNN algorithm for inference.