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In machine learning, backpropagation [1] is a gradient estimation method commonly used for training a neural network to compute its parameter updates. It is an efficient application of the chain rule to neural networks.
Neural backpropagation is the phenomenon in which, after the action potential of a neuron creates a voltage spike down the axon (normal propagation), another impulse is generated from the soma and propagates towards the apical portions of the dendritic arbor or dendrites (from which much of the original input current originated).
Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks, such as Elman networks. The algorithm was independently derived by numerous researchers.
Backpropagation; Rescorla–Wagner model – the origin of delta rule; References This page was last edited on 28 February 2025, at 12:24 (UTC) ...
The standard method for training RNN by gradient descent is the "backpropagation through time" (BPTT) algorithm, which is a special case of the general algorithm of backpropagation. A more computationally expensive online variant is called "Real-Time Recurrent Learning" or RTRL, [ 78 ] [ 79 ] which is an instance of automatic differentiation in ...
Backpropagation through structure (BPTS) is a gradient-based technique for training recursive neural networks, proposed in a 1996 paper written by Christoph Goller and Andreas Küchler. [ 1 ] References
Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks. This is a first-order optimization algorithm. This algorithm was created by Martin Riedmiller and Heinrich Braun in 1992. [1]
Backpropagation training algorithms fall into three categories: steepest descent (with variable learning rate and momentum, resilient backpropagation);; quasi-Newton (Broyden–Fletcher–Goldfarb–Shanno, one step secant);