Normalize any Continuously Distributed Data with a Couple of Lines of Code

How to use inverse transform sampling to improve your model

Normalizing data is a common task in data science. Sometimes it allows us to speed up gradient descent or improve model accuracy, and in some cases it absolutely crucial. For example, the model I described in my last article cannot handle targets that are distributed non-normally. Some normalization techniques, like taking a logarithm, may work most of the time, but in this case, I decided to try something that would …

code data data analysis data science distributed distributed data machine learning python statistics