Answer / Shobhit Mishra

Attention mechanisms are a technique in deep learning models that allow them to focus on specific parts of the input when processing sequential data, such as natural language or images. This enables the model to better understand and generate contextually relevant responses. Attention mechanisms can be implemented through various methods like Bahdanau et al.'s (2014) attention mechanism in sequence-to-sequence models and Vaswani et al.'s (2017) multi-head self-attention in Transformer models. The idea is to assign different weights or 'attentions' to each input element, determining the model's focus during processing.

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