Embeddings Turn Token IDs Into Usable Geometry
Describe why token IDs are embedded as vectors before attention can operate on them.
A token ID is an address; an embedding is the learned vector stored at that address. Recall: what the layer does The embedding layer maps each token ID to a dense vector. The ID is a symbolic index. The vector is the numeric object that later layers can transform. Understand: why vectors help Neural networks operate through linear algebra and nonlinear transformations. A raw ID has no meaningful arithmetic structure. A vector can be projected into queries, keys, values, and feed-forward transformations. Analyze: what changes later The initial embedding is not the whole meaning. After attention runs, the representation for…
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