Data normalization of count data for neural networks

Question

I have a sparse matrix of count data that I'm using as input to a neural network.

I know, usually, the input data should be normalized (e.g. via min-max scaling, $z$-score standardization, etc.). But for features that are counts, what is a good approach? Should I $\log_2(x+1)$ transform the data and then do a $z$-score standardization? Is there another better approach?

Answer 1

One option is to convert counts to rate. Rates are always bound between 0 and 1. For example instead of a count of 100 events, the data could be encoded as a rate of .10 (100 events out of 1,000 opportunities).