Course

Embeddings & Semantic Search

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Modules

2 total

Embedding as a dense vector in a learned space

Explain the chain from token IDs → token embeddings → pooled text embeddings.

Vector indexes: how semantic search scales

I can explain why exact (brute-force) nearest-neighbor search stops scaling and how approximate nearest-neighbor indexes trade a measurable amount of recall for large speed and memory wins.

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