AI Foundationspredict · compress · act

concepts → Algorithm

Algorithm

Gradient descent

timeless3 connections

walk downhill on the loss by following its slope

the formal statement
θ ← θ − η ∇L(θ)

Where it sits

The prism has six jobs across and eight layers down. Its primary cell islearn × L4, with a noted second cell. Hatched cells cannot exist — a GPU does not learn, an institution does not infer.

learn×L0 — it is a general optimisation method, not a neural-network trick

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.

Learning rateGradient descentnothing depends on it yet — a leaf in the reading order

Before it: Learning rate

What kind of thing it is — and what it is made of

It is made of: Learning rate.

Where to read it

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

10Rolling Downhillact 2 · The Instruments

Where it comes from

paperLearning Representations by Back-Propagating ErrorsDavid Rumelhart, Geoffrey Hinton, Ronald Williams · 1986

Every connection

All 3 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
has as a partLearning rateQuantity
Order · 1
requiresLearning rateQuantity
Flow · 1
optimisesLoss functionObjective

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.