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A neural network architecture built around the attention mechanism, introduced in 2017. Transformers replaced recurrence with parallel attention layers, which made it practical to train models on enormous text corpora. Almost every modern large language model uses some variant of the transformer architecture.
If you can read the original transformer paper and explain why attention beat recurrence, you can hold your own in most AI engineering interviews. Knowing the architecture matters when you reason about cost, latency, and scaling decisions.
A neural network architecture built around the attention mechanism, introduced in 2017. Transformers replaced recurrence with parallel attention layers, which made it practical to train models on enormous text corpora. Almost every modern large language model uses some variant of the transformer architecture.
If you can read the original transformer paper and explain why attention beat recurrence, you can hold your own in most AI engineering interviews. Knowing the architecture matters when you reason about cost, latency, and scaling decisions.
Definitions are original explanations written for career development purposes. For authoritative technical definitions, refer to NIST, ISO, or the relevant standards body.
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