gradient descent
An iterative optimization method that adjusts parameters by following the negative gradient of a loss function, fundamental to training neural networks.
1 milestone
Backpropagation Described by Arthur E. Bryson Jr. and Yu-Chi Ho
In 1969, Arthur E. Bryson Jr. and Yu-Chi Ho of Harvard University described a gradient-based optimisation procedure for multi-stage dynamic systems in their textbook Applied Optimal Control, presenting what is now recognised as an early statement of the backpropagation principle in a supervised-learning context.