# SVD - vectors in matrix A

In SVD we have $$A = U \Sigma V^T$$. When applying it for ML, e.g. to calculate Moore-Penrose pseudoinverse for linear regression, I have seen that we take columns of $$A$$ as vectors. Typically in ML I have seen rows as vectors, i.e. matrix $$A$$ is a collection of $$n$$ measurements, which form rows, and columns are $$d$$ dimensions.

1. In case of SVD, why do we assume column vectors instead?
2. If I wanted to use row vectors instead, should I just do $$A = V \Sigma U^T$$?