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Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single input–output example, and does so efficiently, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this can be derived through ...
Then, the backpropagation algorithm is used to find the gradient of the loss function with respect to all the network parameters. Consider an example of a neural network that contains a recurrent layer and a feedforward layer . There are different ways to define the training cost, but the aggregated cost is always the average of the costs of ...
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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).
[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]). In 1986, David E. Rumelhart et al. popularised backpropagation but did not cite the original work. [29] [8]
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).
“For example, listening to a podcast or chatting to a friend when I’m running takes my mind off the fact that my legs hurt! But I’m intentionally taking attention away from one thing onto ...
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]