The transformer is the architecture behind modern large language models, and it has spread into time series, vision, and quantitative finance. Its core idea, attention, is simpler than it sounds: instead of passing information down a chain of hidden states the way a recurrent network does, attention lets every position in a sequence look directly at every other position and decide what is relevant. That one change removes the recurrent bottleneck, captures long-range dependencies in a single step, and parallelizes across the sequence. This lesson builds attention from the query, key, value idea, works a scaled dot-product attention calculation by hand on short vectors, and then assembles the full transformer block. Attention is now a standard interview topic even outside pure deep learning roles, so being able to compute it on a small example is worth the practice.
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