Attention Mechanisms
Attention Mechanisms are a type of machine learning technique that allows models to focus on specific parts of input data when making predictions. It is a method of assigning weights to different parts of the input data, allowing the model to selectively focus on the most relevant information. Attention mechanisms have been widely used in natural language processing tasks such as machine translation, text summarization, and sentiment analysis. They have also been applied to computer vision tasks such as image captioning and object recognition. Attention mechanisms have been shown to improve the performance of machine learning models, particularly in tasks where long sequences of input data are involved.
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