Mean square error (MSE) is the average squared loss per example over the whole dataset. To calculate MSE, sum up all the squared losses for individual examples and then divide by the number of examples
You iterate until overall loss stops changing or at least changes extremely slowly. When that happens, we say that the model has converged.
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