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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.
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.
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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).
In 1970, Seppo Linnainmaa published the modern form of backpropagation in his master thesis (1970). [23] [24] [13] G.M. Ostrovski et al. republished it in 1971. [25] [26] Paul Werbos applied backpropagation to neural networks in 1982 [7] [27] (his 1974 PhD thesis, reprinted in a 1994 book, [28] did not yet describe the algorithm [26]).
steepest descent (with variable learning rate and momentum, resilient backpropagation); quasi-Newton ( Broyden–Fletcher–Goldfarb–Shanno , one step secant ); Levenberg–Marquardt and conjugate gradient (Fletcher–Reeves update, Polak–Ribiére update, Powell–Beale restart, scaled conjugate gradient).
Seppo Linnainmaa in 1970 is said to have developed the Backpropagation Algorithm [7] but the origins of the algorithm go back to the 1960s with many contributors. It is a generalisation of the least mean squares algorithm in the linear perceptron and the Delta Learning Rule.
They, ESNs and the newly researched backpropagation decorrelation learning rule for RNNs [7] are more and more summarized under the name Reservoir Computing. Schiller and Steil [7] also demonstrated that in conventional training approaches for RNNs, in which all weights (not only output weights) are adapted, the dominant changes are in output ...