A convolutional neural network (CNN) is a type of deep learning model designed to process structured grid data like images. It uses convolutional layers to extract features such as edges, textures, and patterns, making it highly effective for image recognition, classification, and segmentation tasks. The architecture includes convolutional layers, pooling layers, and fully connected layers. Convolutional layers apply filters to input data, generating feature maps that capture essential details. Pooling layers then reduce the spatial dimensions, retaining important features while lowering computational cost. CNNs are widely used in applications like facial recognition, object detection, and medical imaging. For instance, in autonomous driving, CNNs help identify pedestrians, vehicles, and traffic signs, enabling the car to make informed decisions.
What is a convolutional neural network in image processing?
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