OBJECT RECORD / AIM-DOC-1986-001
Learning representations by back-propagating errors
A concise account of adjusting network weights by propagating output error backward.
CURATORIAL READING
Why it is here
Rumelhart, Hinton, and Williams described repeatedly changing connection weights to reduce the difference between actual and desired outputs, allowing hidden units to acquire useful task features.
Evidence boundary
The object documents one influential 1986 formulation. It does not establish that every later learning system uses the same procedure, or that the authors invented every ingredient.
05 / LEARN REPRESENTATIONS
Position in the guided path
From written rules to learned representations
Weights change as output error travels backward through the network.
Back to the object room →PROVENANCE
Source trail
Nature ·