Resource listing models with known VC dimension
Rather than computing the VC dimension of a particular function class, it's usually more interesting to understand how generic properties of a function class relate to its VC dimension. For example, function spaces with linear dimension $d$ have VC-dim at most $d$. You can also bound the VC-dim of a function class realized by circuits with bounded depth/fanin (Lemma 8.6 in Bartlett and Anthony, Neural Network Nearning) or the VC-dim of a neural network with a certain number of nodes (Thm. 3.6 in Kearns &amp; Vazirani,  An Introduction to Computational Learning Theory.