What is a Convolutional Neural Network (CNN)?

A Convolutional Neural Network (CNN) is a type of neural network that is primarily used for image recognition and computer vision tasks. CNNs are a specialized form of neural network architecture designed to automatically and adaptively learn spatial hierarchies of features from input data.

Architecture of CNNs

The key components of a CNN include convolutional layers, pooling layers, and fully connected layers. Convolutional layers apply learnable filters or kernels over the input image to extract features. Pooling layers downsample the spatial dimensions of the feature maps generated by convolutional layers. Fully connected layers are traditional neural network layers that classify the extracted features.

Feature Learning in CNNs

CNNs excel at feature learning by automatically identifying patterns in images without the need for manual feature extraction. This ability makes them well-suited for tasks such as object detection, facial recognition, and image classification. By learning hierarchical features from raw data, CNNs can generalize well to unseen data.

Applications of CNNs

CNNs are widely used in various fields such as medicine (medical image analysis), autonomous vehicles (object detection), agriculture (crop monitoring), and security (surveillance systems). They have revolutionized the field of computer vision and have become a cornerstone of deep learning and artificial intelligence.