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Convolutional Neural Networks (CNNs) are a type of deep learning algorithm designed to process data with grid-like topology, such as images. They excel in image processing tasks due to their ability to automatically learn spatial hierarchies of features through the use of convolutional layers. These layers apply a series of filters that detect patterns like edges and shapes at various scales, enabling CNNs to recognize increasingly complex visual features as they progress deeper into the network. CNNs consist of three primary building blocks: convolutional layers, pooling layers, and fully connected layers. The convolutional layer applies a specified number of filters to the input data, generating feature maps that highlight specific features present within the input. The pooling layer downsamples the feature maps by taking the maximum or average value within a defined neighborhood, reducing dimensionality while preserving essential information. Lastly, fully connected layers connect every neuron in one layer with all neurons in another layer, forming a traditional multi-layer perceptron (MLP) for image classification tasks. CNNs play an integral role in the realm of Artificial Intelligence Algorithms under Image Processing, as they enable advanced Computer Vision applications within Technology and Artificial Intelligence. These powerful models can detect objects within images, recognize facial expressions, read text characters from images, and even generate new image content through techniques like style transfer or image generation. By combining multiple convolutional layers with various filter sizes and properties, CNNs construct a robust hierarchy of learned features that progressively extract higher-level abstractions from raw input data. This hierarchical structure enables them to outperform traditional image processing methods by automatically learning salient features instead of relying on handcrafted feature extraction techniques. In summary, Convolutional Neural Networks are a specialized class of deep learning algorithms that excel in image processing tasks. They build upon the broader concepts of Artificial Intelligence Algorithms and Computer Vision to provide state-of-the-art performance across numerous applications within Technology and Artificial Intelligence. CNNs' unique ability to automatically learn hierarchical feature representations from grid-like data sets them apart as a powerful tool for addressing complex image processing challenges.