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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).
Back_Propagation_Through_Time(a, y) // a[t] is the input at time t. y[t] is the output Unfold the network to contain k instances of f do until stopping criterion is met: x := the zero-magnitude vector // x is the current context for t from 0 to n − k do // t is time. n is the length of the training sequence Set the network inputs to x, a[t ...
The terminology "back-propagating errors" was actually introduced in 1962 by Rosenblatt, [24] but he did not know how to implement this, although Henry J. Kelley had a continuous precursor of backpropagation in 1960 in the context of control theory. [40] In 1970, Seppo Linnainmaa published the modern form of backpropagation in his Master's ...
The terminology "back-propagating errors" was actually introduced in 1962 by Rosenblatt, [16] but he did not know how to implement this, although Henry J. Kelley had a continuous precursor of backpropagation in 1960 in the context of control theory. [26] The modern form of backpropagation was developed multiple times in early 1970s.
The standard method is called "backpropagation through time" or BPTT, a generalization of back-propagation for feedforward networks. [ 45 ] [ 46 ] A more computationally expensive online variant is called " Real-Time Recurrent Learning " or RTRL.
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]
When combined with the back propagation algorithm, it is the de facto standard algorithm for training artificial neural networks. [23] Its use has been also reported in the Geophysics community, specifically to applications of Full Waveform Inversion (FWI).