concepts → Algorithm
Backpropagation
assign blame for the error backwards through the layers, then nudge every weight
Where it sits
The prism has six jobs across and eight layers down. Its primary cell islearn × L1, with a noted second cell. Hatched cells cannot exist — a GPU does not learn, an institution does not infer.
learn×L4 — it is the engine of every training run
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.
Before it: Loss function · Perceptron
Unlocks: Pretraining
What kind of thing it is — and what it is made of
It is made of: Backpropagation through time.
Where to read it
The chapter that introduces it, and any chapter that uses it again.
Where it comes from
Every connection
All 4 edges touching this node, grouped by relation family — the sections above are highlights from this list. Colours match the relation families inthe atlas.
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.