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In the end , what matters is your chosen metrics, not the loss. The loss is just a function we’ve give the computer to help us to optimize.o
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Machines learn by means of a loss function. It’s a method of evaluating how well specific algorithm models the given data. If predictions deviates too much from actual results, loss function would cough up a very large number.
Thinks like Cross Entropy , binary-cross-entropy and SoftMax & ArgMax , Mean Absolute Error (MAE), Root mean Squared Error (RMSE),… are all loss functions. See overview here.
A great explanation, given by fastai, can be found here. It covers the math and explains how and why to apply it.