What does "backprop" mean? Is the "backprop" term basically the same as "backpropagation" or does it have a different meaning?
'Backprop' is short for 'backpropagation of error' in order to avoid confusion when using backpropagation term.
Basically backpropagation refers to the method for computing the gradient of the case-wise error function with respect to the weights for a feedforward networkWerbos. And backprop refers to a training method that uses backpropagation to compute the gradient.
So we can say that a backprop network is a feedforward network trained by backpropagation.
The 'standard backprop' term is a euphemism for the generalized delta rule which is most widely used supervised training method.
Source: What is backprop? at FAQ of Usenet newsgroup comp.ai.neural-nets
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Yes, as Franck has rightly put, "backprop" means backpropogation, which is frequently used in the domain of neural networks for error optimization.
For a detailed explanation, I would point out this tutorial on the concept of backpropogation by a very good book of Michael Nielsen.
In machine learning, backpropagation (backprop, BP) is a widely used algorithm in training feedforward neural networks for supervised learning. Generalizations of backpropagation exist for other artificial neural networks (ANNs), and for functions generally – a class of algorithms referred to generically as "backpropagation".