<ul><li>The Attention Mechanism is crucial in tasks like Machine Translation to focus on important words for prediction.</li><li>It helped RNNs mitigate the vanishing gradient problem and capture long-range dependencies among words.</li><li>Self-attention in Transformers provides information on the correlation between words in the same sequence.</li><li>It generates attention weights for each token based on other tokens in the sequence.</li><li>By multiplying query and key vectors and applying softmax, attention weights are obtained.</li><li>Multi-head Self-Attention in Transformers uses multiple sets of matrices to capture diverse relationships among tokens.</li><li>The dense vectors from each head are concatenated and linearly transformed to get the final output.</li><li>The implementation involves generating query, key, and value vectors for each token and calculating attention scores.</li><li>Softmax is applied to get attention weights, and the final context-aware vector is computed for each token.</li><li>A multi-head attention mechanism with separate weight matrices for each head is used to improve relationship capture.</li></ul>

A Simple Implementation of the Attention Mechanism from Scratch

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