Backpropagation

Efficient reverse-mode computation of gradients through a layered model, anchored at Rumelhart, Hinton & Williams ("Learning representations by back-propagating errors," Nature 323, 1986), whose specific contribution was the first empirical demonstration that backprop-trained hidden layers learn useful internal representations -- not just wider distribution of an existing algorithm. Rumelhart et al. independently re-derived the underlying technique without citing prior work. Split (2026-09-03) from its general mathematical ancestor, Reverse-Mode Automatic Differentiation (Linnainmaa, 1970, parent_node_id above) -- per BASIC_METHODOLOGY.md Tier 1 SS4 Option B, now that Basic has scaled past the 3-node POC that originally justified a single compromise-dated node (Option A). Werbos (1974 PhD thesis; concrete application in 1982) was arguably first to apply the technique to a neural-network-style model -- left as historical context here rather than modeled as a third node, since that claim is gradual/multi-source rather than one clean citable result to split off.