A Convolutional Neural Network (CNN) is a type of deep learning algorithm designed for processing structured grid data, such as images. It consists of multiple layers, including convolutional layers, pooling layers, and fully connected layers, which work together to automatically and adaptively learn spatial hierarchies of features from input data.
CNNs are used primarily for image and video recognition, classification, and processing. They are effective in these tasks because they can automatically detect important features without requiring manual feature extraction. This makes CNNs valuable for applications like object detection, facial recognition, and medical image analysis.
Yes, CNN stands for Convolutional Neural Network. It is named for the convolution operation that is a core component of its architecture, which helps in extracting features from input data through a series of transformations.
In a CNN, a convolution is an operation where a filter (or kernel) is applied to an input data matrix (such as an image) to produce a feature map. This process involves sliding the filter across the input and computing the dot product between the filter and portions of the input data. Convolutions help in capturing local patterns and spatial hierarchies in the data.
A CNN works step by step as follows: First, the input image is passed through a series of convolutional layers where filters extract features. Next, pooling layers reduce the spatial dimensions of the feature maps, retaining important features while reducing computational complexity. This process of convolution and pooling may be repeated multiple times. Finally, the output feature maps are flattened and passed through fully connected layers to produce the final classification or prediction.
