What is a Convolutional Neural Network?

Convolutional Neural Networks (CNNs) are a specialized deep learning architecture designed to handle complex image classification and object detection tasks by mimicking human visual processing through the use of convolutional layers. CNNs apply small matrices, known as filters or kernels, across images to extract essential features like edges and textures, which are then refined through pooling and activation functions to create a compact representation of the original image. Key features of CNNs include their ability to handle large datasets, robustness to translation and rotation, and adaptability through transfer learning. However, they require significant computational resources and can be prone to overfitting. Notable examples of CNN architectures include AlexNet, which revolutionized image recognition with its performance in the 2012 ImageNet competition, and newer models like ResNet and MobileNet, which address challenges such as vanishing gradients and computational efficiency. CNNs have become instrumental in computer vision applications, enhancing tasks like semantic segmentation and object detection through sophisticated layers and techniques, and while they are powerful, they also present challenges related to computational demands and explainability.