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Convolutional Neural Networks (CNNs) are a type of neural network architecture, specifically designed for processing grid-like data, such as images. They have proven to be highly effective in image recognition and classification tasks due to their ability to automatically learn and extract relevant features from raw input data. CNNs consist of multiple layers that process the information using convolutions, pooling, and fully connected layers. Convolution layers apply a set of filters or kernels to the input data, resulting in feature maps that highlight specific patterns or features. Pooling layers reduce the spatial dimensions of these feature maps while retaining important information, thus controlling overfitting and computational complexity. Fully connected layers then combine this information for the final classification or regression task. Within the context of artificial intelligence algorithms and neural network architectures, CNNs serve as an essential tool in unsupervised learning scenarios. These networks can learn representations directly from image data without requiring labeled examples, thus enabling applications such as anomaly detection, dimensionality reduction, and clustering in various technology and AI domains. As a result, CNNs have become a crucial component of artificial intelligence algorithms, offering powerful capabilities for processing grid-like data and uncovering patterns within it.