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Attention networks are neural network architectures that dynamically focus on specific parts of input data, enhancing the model's ability to handle complex tasks by prioritizing relevant information. This mechanism is crucial in applications like natural language processing and computer vision, where it improves interpretability and efficiency by reducing the cognitive load on the network.
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