What is cross entropy and why is it useful?

What is cross entropy and why is it useful?



Cross Entropy is a loss function in which error has to be minimized. Cross Entropy is the summation of negative logarithmic probabilities, logarithmic values are used for numerical stability. Cross Entropy compares the distance between the outputs of softmax and one-hot encoding. Neural Networks estimate the probability of the given data for every class. Probability has to be maximized to the correct target label.

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