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Convolutional Neural Networks (CNNs) are a type of neural network architecture designed to process data with grid-like topology, such as an image, by employing a mathematical operation called convolution. They excel in recognizing patterns and structures within images, making them indispensable for various computer vision tasks. CNNs consist of layers like convolutional layers, pooling layers, and fully connected layers, enabling feature extraction, dimensionality reduction, and classification. In the hierarchy "Technology/Artificial Intelligence/Deep Learning/Long Short Term Memory Networks", CNNs are a specialized subset that focuses on spatial hierarchies of features and patterns in data, enhancing their applicability within the broader realms of artificial intelligence and deep learning. They can also be combined with recurrent neural networks (RNNs) to create architectures like ConvLSTM, which effectively models temporal dynamics alongside spatial information in sequences of images or videos.