Convolutional Neural Networks (CNNs) - Artificial Cognition and Machine Technology Today

Convolutional Neural Networks (CNNs) are a specialized class of deep learning algorithms designed primarily for processing structured grid data, such as images. Inspired by the visual cortex of animals, CNNs utilize convolutional layers to automatically detect and learn features from the input data by applying filters (kernels) that slide over the image, capturing spatial hierarchies and patterns. These networks typically consist of multiple layers, including convolutional layers, activation functions (such as ReLU), pooling layers to down-sample the feature maps, and fully connected layers for classification tasks. CNNs excel in tasks such as image recognition, object detection, and segmentation, making them the backbone of many computer vision applications. Their success is attributed to their ability to learn hierarchical representations, requiring less manual feature extraction compared to traditional machine learning methods, and they have been instrumental in advancements in fields ranging from healthcare to autonomous vehicles and facial recognition.