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Convolutional Neural Networks, or CNNs, are neural networks with a specialized architecture designed to process grid-like data, most commonly images. They consist of convolutional layers that apply filters to input data to extract features, pooling layers that downsample the spatial dimensions, and fully connected layers that perform the final classification. This hierarchical extraction of features allows CNNs to learn complex patterns in data and achieve impressive results in image recognition tasks. In the context of Technology, Artificial Intelligence, and Neural Network Architectures, CNNs stand out as a powerful tool for leveraging the computational power of modern hardware to analyze visual data, driving advancements in fields like computer vision, autonomous vehicles, and medical imaging.