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What is the difference between Loss and RMSE when do regression task using the Deep Learning Tool Box?

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I'm doing a regression task using Deep Learning Tool Box, and the Training Progress showing two classes of curves namely RMSE and Loss.
What is the difference between? I cann't find detailed description In the Help document.

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Shreya Bhatia
Shreya Bhatia on 22 Jan 2020
Hi Zongwei, May I know the nature of your project? I am doing a similar regression project that I am doing to predict the gait cycle % of when a person is climbing stairs. I am using RNN for it. My regression values are linearly continuous between 0 and 100%. Do let me know your approach?

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Accepted Answer

Deepak Kumar
Deepak Kumar on 17 Oct 2019
Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit.
A loss function is a measure of how good a prediction model does in terms of being able to predict the expected outcome.
To know more about RMSE and Loss refer to following links:

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Zongwei Yao
Zongwei Yao on 17 Oct 2019
Thanks for the lovely advice.
I watched the video and understood the defination of cross-entropy for classification task,.
I'm wondering does the cross-entropy works for regression case as the same? How the probability is calculated?

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