The cross entropy can be understood as the expected number of bits you need to specify some event if you assume an underlying distribution. It is large if your

The cross entropy can be understood as the expected number of bits you need to specify some event if you assume an underlying distribution. It is large if your assumed distribution is very different from the true distribution. This is indeed helpful intuition for making sense of formulas in machine learning.