What are the major differences between cost, loss, error, fitness, utility, objective, criterion functions?
Why is the Jensen-Shannon divergence preferred over the KL divergence in measuring the performance of a generative network?
Why is the "square error function" sometimes defined with the constant 1/2 and sometimes with the constant 1/m?
How can the sum of squared errors have negative gradient if it's defined as the squared of the error?
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