In 2017, eight researchers at Google published a paper called "Attention Is All You Need." The title was a provocation. The dominant view was that you needed recurrent networks to process sequences. You needed memory and sequential processing. The paper said you needed none of that. Just attention, applied in a smart architecture. The result was faster to train, easier to parallelize, and better on nearly every benchmark. The transformer they described in that paper is the direct ancestor of BERT, GPT-2, GPT-3, GPT-4, Claude, Gemini, and every other large language model that has changed how we interact with computers. This post builds the full transformer encoder from scratch. You already have all the pieces from the last post. This is where they come together. The Full Architecture A transformer has two main components: Encoder: reads the input sequence and builds a rich contextual representation. BERT is an encoder. Used for understanding tasks: classification, named entity recognition, question answering.…