Convolutional Networks

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Convolutional neural networks (CNNs) are deep learning models designed to work especially well with image data. Instead of connecting every pixel to every neuron, CNNs use small filters that move across the image and detect local patterns such as edges, textures, or simple shapes. As the network goes deeper, it combines these small patterns into more complex features, allowing it to recognize objects or scenes.

A typical CNN includes convolution layers that learn the filters, pooling layers that shrink the image while keeping important information, and fully connected layers that use the learned features to make final predictions. CNNs are widely used for tasks like image classification, object detection, or medical image analysis. They can also handle time-series signals and some types of text data.

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