# How does multicollinearity affect neural networks?

Multicollinearity is a problem for linear regression because the results become unstable / depend too much on single elements (source).

(Also, the inverse of $X^TX$ doesn't exist so the standard OLS estimator does not exist ... I have no idea how, but sklearn deals with it just fine)

Is (perfect) multicollinearity also a problem for neural networks?