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What does "backprop" mean? Is the "backprop" term basically the same as "backpropagation" or does it have a different meaning?

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"Backprop" is the same as "backpropagation": it's just a shorter way to say it. It is sometimes abbreviated as "BP".

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'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

References:

  • Werbos, P. J. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD thesis, Harvard University.
  • Werbos, P. J. (1994). The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting,Wiley Interscience.
  • Bertsekas, D. P. (1995), Nonlinear Programming, Belmont, MA: Athena Scientific, ISBN 1-886529-14-0.
  • Bertsekas, D. P. and Tsitsiklis, J. N. (1996), Neuro-Dynamic Programming, Belmont, MA: Athena Scientific, ISBN 1-886529-10-8.
  • Polyak, B.T. (1964), "Some methods of speeding up the convergence of iteration methods," Z. Vycisl. Mat. i Mat. Fiz., 4, 1-17.
  • Polyak, B.T. (1987), Introduction to Optimization, NY: Optimization Software, Inc.
  • Reed, R.D., and Marks, R.J, II (1999), Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, Cambridge, MA: The MIT Press, ISBN 0-262-18190-8.
  • Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986), "Learning internal representations by error propagation", in Rumelhart, D.E. and McClelland, J. L., eds. (1986), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1, 318-362, Cambridge, MA: The MIT Press.
  • Werbos, P.J. (1974/1994), The Roots of Backpropagation, NY: John Wiley & Sons. Includes Werbos's 1974 Harvard Ph.D. thesis, Beyond Regression.
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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.

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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".

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  • $\begingroup$ Hi, welcome to the AI Stack Exchange. Rather than posting answers from Wikipedia, it is encouraged to express answers to questions in one owns words though Wikipedia can be used to support an argument or help derive an explanation. Please update your answer to be more along these lines. $\endgroup$ – Jaden Travnik Apr 22 at 14:14
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It's a fancy name for the multivariable chain rule.

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  • $\begingroup$ Hello. Could you support this answer with a little more detail? (I realize it's a simple question, but I think your answer is worthy of greater explication.) $\endgroup$ – DukeZhou Apr 23 at 21:06

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