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Convolutional Neural Networks (CNNs) are a specialized class of artificial neural networks designed to tackle computer vision tasks, such as image and video recognition, by automatically learning spatial hierarchies of features from data. CNNs are deeply rooted in the field of Data Modeling within Data Science, leveraging Technology advancements to process large-scale, high-dimensional visual data efficiently. As a crucial component of Deep Learning Neural Networks, CNNs consist of layers that perform convolution and pooling operations, followed by fully connected layers responsible for classification tasks. Convolution layers apply filters or kernels to extract low-level features like edges and shapes, then gradually progress to learning high-level abstract representations in deeper layers. Pooling layers reduce the spatial dimensions while retaining essential information, improving computational efficiency and reducing overfitting risks. CNNs have revolutionized various industries by enabling accurate image analysis in healthcare, autonomous vehicles, surveillance systems, facial recognition, and augmented/virtual reality applications, among others. By capitalizing on hierarchical feature learning, CNNs empower Data Science professionals to unlock deeper insights from complex visual data, driving innovation and growth across Technology-centric domains.