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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.
In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes encountered when training neural networks with backpropagation.In such methods, neural network weights are updated proportional to their partial derivative of the loss function. [1]
Backpropagation; Rescorla–Wagner model – the origin of delta rule; References This page was last edited on 27 October 2023, at 04:45 (UTC). ...
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).
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 ...
Paul John Werbos (born September 4, 1947) is an American social scientist and machine learning pioneer. He is best known for his 1974 dissertation, which first described the process of training artificial neural networks through backpropagation of errors. [1]
Backpropagation was first described in 1986, with stochastic gradient descent being used to efficiently optimize parameters across neural networks with multiple hidden layers. Soon after, another improvement was developed: mini-batch gradient descent, where small batches of data are substituted for single samples.