  We prove Carl's type inequalities for the error of approximation of compact
sets K by deep and shallow neural networks. This in turn gives lower bounds on
how well we can approximate the functions in K when requiring the approximants
to come from outputs of such networks. Our results are obtained as a byproduct
of the study of the recently introduced Lipschitz widths.
