The 15-Day AI/ML Interview Bootcamp
Day 12 — Attention and the transformer, from scratch
Embeddings, queries, keys and values, scaled dot-product attention, multi-head attention, positional encoding, the encoder block with residuals and layer norm, and why the transformer replaced the RNN — built from scratch, with the attention questions every machine learning interview asks.
"The cat drank the milk because it was thirsty." You read that and knew, without slowing down, that it was the cat and not the milk. Swap thirsty for cold and you'd flip your answer just as fast. Yesterday's recurrent cell had to carry "cat" through five squashes to get it to "it", and by Day 11's own numbers most of it leaked out on the way. Then an interviewer asks the question every modern ML round now opens with: "Walk me through self-attention. Why the square root? Why more than one head?" Today you and I build the mechanism that made the leak unnecessary — a lookup, not a relay — and the…
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