y logistic regression case can be generalized to multinomial logistic regression, in which
we model the probability that i is one of the classes using this formula, which is also known as softmax.
The probability is proportional to e to the minus $w^t x$ which is the same as in the binary case.
But now we need to normalize it so that the sum over all classes is one. So we just divide it by this sum.


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