What are the full steps of causal self-attention in Vizuara's attention family series?

Sreedath Panat's summary pipeline projects input tokens into query, key and value spaces with matrices WQ, WK and WV, takes query-key dot products as attention scores, divides them by the square root of the key dimension d, sets the upper triangle to minus infinity, applies softmax, applies dropout with rescaling, and multiplies the result by the value vectors to get context vectors. The notebook example projects six three-dimensional embeddings for Dream big and work for it into a two-dimensional query, key and value space.

• Lecture 5 covered self-attention without trainable weights and Lecture 6 added trainable weights.
• Lecture 8 extends the single head to multi-head attention, which Panat calls one of the last lectures of the series' first module.

Source: our own transcription of the episode. The full conversation is on The attention family (2026-09-24). Read the whole episode summary →

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