How Attention Powers Transformers

This animation introduces the transformer architecture by visualizing how attention lets a model weigh the relevance of every word in a sentence when interpreting another word. It shows tokens turning into vectors, query-key-value matching, and attention scores forming connections between words, building intuition for why transformers handle context better than older sequence models. Useful for beginners in machine learning or NLP who want a visual, non-mathematical first look at how modern language models process text.

Narrated · 16:9 · Preview before teaching · automatic layout checks do not establish subject accuracy

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Explain transformers and attention Audience: newbie

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• Show how self-attention scores are calculated step by step with query, key, and value vectors
• Visualize multi-head attention and why multiple heads capture different relationships
• Compare transformers to RNNs to show why parallel attention improved language modeling