# Why regularization parameter called as lambda in theory and alpha in python?

I was learning about regularization and came across the term called regularization parameter.

I see that it is called lambda in theory but when I looked at the python implementation, I see that it is denoted as alpha. Here is the link1 and link2

Am I right to understand that both mean the same?

Is there any difference between regularization paramter lambda and regularization parameter alpha ?

In this particular case, the word lambda is reserved by the Python language, so alpha avoids overlapping with that word.
As an aside, one sharp corner in sklearn is that sklearn.linear_model.LogisticRegression uses the inverse of regularization strength as the regularization parameter, so $$C=\lambda^{-1}$$. In a different package, you might set $$\lambda=10$$, but for this class, you would get an equivalent result with $$C=0.1$$.