Attention and the transformer

After this lesson you can: Explain what 'attention' lets a model do that earlier models could not.

First: Embeddings: meaning as coordinates

Watch

• Attention in transformers, step-by-step | Deep Learning Chapter 6 - 3Blue1Brown, 26 min. The title directly targets attention step-by-step, at concept length, from a highly visual teaching channel.
• Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!! - StatQuest with Josh Starmer, 36 min. A clearly explained transformer overview is well matched to demystifying attention for non-ML engineers.
• Transformers Explained | Simple Explanation of Transformers - codebasics, 58 min. The simple transformer explanation and longer concept duration suit learners needing context beyond embeddings.

Notes

Attention is a way for a model to decide which previous tokens are relevant when processing the current token. Earlier sequence models had to squeeze the past through a single running state, which made long-range dependencies fragile. Attention instead lets each token look across the available context and pull information from the tokens that matter most for this computation.

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