Both are fine. Empirically, the cross tends to result in better performance of the resulting models though. Also, the the derivative/gradient calculation plays nicely with sigmoid or softmax in the last layer, i.e., it simplifies as follows (where “a” is the sigmoid activation, y is the true label, and z is the weighted input w^T x):

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