The partition function is an integral or sum over unnormalized probabilities. This function, often denoted by Z, is used to normalize an unnormalized distribution into a valid distribution. $p(\textbf{x}) = \frac{1}{Z} \tilde{p}(\textbf{x}) .$

where Z is:

$Z = \int \tilde{p}(\textbf{x}) d\textbf{x}.$ or $Z = \sum_{x} \tilde{p}(\textbf{x}) d\textbf{x}.$

Z = partition function.
$\tilde{p}(\textbf{x})$ = Unnormalized distribution. $p(\textbf{x})$= Normalized distribution. In the context of deep learning, Z is usually intractable, meaning that it often needs to be approximated.