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"6.5 Back-Propagation and Other Differentiation Algorithms". Deep Learning. MIT Press. pp. 200– 220. ISBN 9780262035613. Nielsen, Michael A. (2015). "How the backpropagation algorithm works". Neural Networks and Deep Learning. Determination Press. McCaffrey, James (October 2012). "Neural Network Back-Propagation for Programmers". MSDN Magazine.
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 ...
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).
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Backpropagation allowed researchers to train supervised deep artificial neural networks from scratch, initially with little success. Hochreiter 's diplom thesis of 1991 formally identified the reason for this failure in the "vanishing gradient problem", [ 2 ] [ 3 ] which not only affects many-layered feedforward networks , [ 4 ] but also ...
However, when back-propagation through time is applied, additional processes are needed because updating input and output layers cannot be done at once. General procedures for training are as follows: For forward pass, forward states and backward states are passed first, then output neurons are passed.
Artificial neural network architectures are based on inputs multiplied by weights to obtain outputs (inputs-to-output): feedforward. [2] Recurrent neural networks, or neural networks with loops allow information from later processing stages to feed back to earlier stages for sequence processing. [3]
Networks such as the previous one are commonly called feedforward, because their graph is a directed acyclic graph. Networks with cycles are commonly called recurrent. Such networks are commonly depicted in the manner shown at the top of the figure, where is shown as dependent upon itself. However, an implied temporal dependence is not shown.