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concepts → Algorithm

Algorithm

word2vec

timeless1 connectiondraft

learn a vector per word by predicting its neighbours, cheaply and at scale

Where it sits

The prism has six jobs across and eight layers down. Its primary cell islearn × L1. Hatched cells cannot exist — a GPU does not learn, an institution does not infer.

What must come first — and what it unlocks

Left to right is reading order, derived from the prerequisite_of edges. Nothing here is hand-ordered: the diagram is the graph.

nothing comes first — this is a starting pointword2vecnothing depends on it yet — a leaf in the reading order

Where to read it

The chapter that introduces it, and any chapter that uses it again.

22Words as Coordinatesact 5 · The Connectionist Turn

Where it comes from

paperEfficient Estimation of Word Representations in Vector SpaceTomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean · 2013

Every connection

All 1 edges touching this node, grouped by relation family — the sections above are highlights from this list. Colours match the relation families inthe atlas.

Structure · 1
is an instance ofWord embeddingMechanism

This page is a projection of one node in src/data/concepts.ts. It has no prose file of its own — 230 declared edges produce all 236 of these pages. Edit an edge and both endpoints change.