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A rate is always a gain per some time/step. A rate can exist even if the maximum is never reached. In supervised learning a loss function is defined, which is expected to have a global maximum, that we try to reach by gradient descent. How much closer we get with each timestep/iteration/epoch/batch is the rate of convergence.

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