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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).
Backpropagation through structure (BPTS) is a gradient-based technique for training recursive neural networks, proposed in a 1996 paper written by Christoph Goller and Andreas Küchler. [ 1 ] References
It used convolutions, weight sharing, and backpropagation. [50] [51] In 1988, Wei Zhang applied a backpropagation-trained CNN to alphabet recognition. [52] In 1989, Yann LeCun et al. created a CNN called LeNet for recognizing handwritten ZIP codes on mail. Training required 3 days. [53] In 1990, Wei Zhang implemented a CNN on optical computing ...
The rituals of self-discipline were nothing new. He’d kept a journal since the 8th grade documenting his daily meals and workout routines. As a teenager, he’d woken up to the words of legendary coaches he’d copied from books and taped to his bedroom walls — John Wooden on preparation, Vince Lombardi on sacrifice and Dan Gable on goals.
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]