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here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn http://stackoverflow.com/questions/35116875/backpropagation-error-derivative more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Backpropagation - error derivative up vote 1 down vote favorite I am learning the backpropagation algorithm used to train neural networks. It kind of makes sense, but back propagation there is still one part I don't get. As far as I understand, the error derivative is calculated with respect to all weights in the network. This results in an error gradient whose number of dimensions is the number of weights in the net. Then, the weights are changed by the negative of this gradient, multiplied by the learning rate. This seems about right, but why is the gradient not normalized? What is the rationale behind back propagation algorithm the length of the delta vector being proportional to the length of the gradient vector? neural-network gradient backpropagation share|improve this question asked Jan 31 at 17:27 PEC 153111 add a comment| 1 Answer 1 active oldest votes up vote 2 down vote You can't normalize gradient. Actually in backpropogation you have gradient descent method of error. Instead you normalize and scale your input. And then it will give you proportional movement on the error surface and proportional movement on the error surface will give you faster approach to local or sometimes global minima. Here you can see explanation of what normalization does share|improve this answer answered Jan 31 at 20:19 Yura Zaletskyy 1,85931327 add a comment| Your Answer draft saved draft discarded Sign up or log in Sign up using Google Sign up using Facebook Sign up using Email and Password Post as a guest Name Email Post as a guest Name Email discard By posting your answer, you agree to the privacy policy and terms of service. Not the answer you're looking for? Browse other questions tagged neural-network gradient backpropagation or ask your own question. asked 8 months ago viewed 42 times active 8 months ago Linked 41 Why do we have to normalize the input for an artificial neural network? Related 32Understanding Neural Network Backpropagation5Implementing a perceptron with backpropagation algorithm1How to adjust weights - backp

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