backpropagation
Algorithm that trains neural networks by propagating error gradients backward through layers to update weights. Enabled practical training of multi-layer networks.
4 milestones
Yann LeCun Applies Backpropagation to Handwritten ZIP Code Recognition at AT&T Bell Labs
In 1989, Yann LeCun and colleagues at AT&T Bell Labs published 'Backpropagation Applied to Handwritten Zip Code Recognition', demonstrating that a convolutional neural network trained with backpropagation could read handwritten postal ZIP codes with high accuracy, establishing a template for practical deep learning in computer vision.
ALVINN: An Autonomous Land Vehicle in a Neural Network, Developed by Dean Pomerleau at CMU
In 1989, Dean Pomerleau at Carnegie Mellon University published ALVINN (Autonomous Land Vehicle in a Neural Network), a three-layer backpropagation network trained on road images that steered the CMU Navlab vehicle autonomously, demonstrating that a neural network could learn driving behaviour directly from sensor data.
NETtalk Neural Network Developed by Terrence J. Sejnowski and Charles Rosenberg
Terrence J. Sejnowski of the Salk Institute and Charles Rosenberg of Princeton University developed NETtalk, a feedforward neural network trained to convert English text to speech, publishing the principal account in Complex Systems in 1987. The network learned pronunciation from examples alone, demonstrating that a multi-layer perceptron could acquire a complex linguistic skill without hand-coded rules.
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.