What is a Convolutional Neural Network (CNN)? A Convolutional Neural Network (CNN), also known as ConvNet, is a specialized type of deep learning algorithm mainly designed for tasks that necessitate object recognition, including image classification, detection, and segmentation. R-CNN Region with Convolutional Neural Networks (R-CNN) is an object detection algorithm that first segments the image to find potential relevant bounding boxes and then run the detection algorithm to find most probable objects in those bounding boxes. Convolutional Neural Network Architecture A CNN typically has three layers: a convolutional layer, a pooling layer, and a fully connected layer. Figure 2: Architecture of a CNN (Source) Convolution Layer The convolution layer is the core building block of the CNN. It carries the main portion of the network’s computational load. Convolutional Neural Networks (CNNs) are a type of deep learning model used for image recognition, processing, and classification. With basic CNN architecture, you can automatically and efficiently extract features from input data. But what is CNN in machine learning?
