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## Main Question or Discussion Point

In neural network the learning algorithm ends when the mean squared error value is less than or equal to a value we have precised.

But i don't understand why we are comparing with the mean squared error and not the mean error?

What does the mean squared error represent?

But i don't understand why we are comparing with the mean squared error and not the mean error?

What does the mean squared error represent?