Attention Mechanism
Attention Mechanism is a technique used in deep learning that allows the model to focus on specific parts of the input sequence while processing it. It is inspired by the human visual attention mechanism and is used to improve the performance of sequence-to-sequence models. Attention Mechanism works by assigning weights to different parts of the input sequence based on their relevance to the current output. These weights are learned during the training process and are used to compute a weighted sum of the input sequence, which is then used as the input to the next step of the model. This allows the model to selectively focus on the most important parts of the input sequence, improving its ability to generate accurate outputs.
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