We obtain limiting spectral distribution of empirical conjugate kernel and neural tangent kernel matrices for two-layer neural networks with deterministic data and random weights. When the width of the network grows faster than the size of the dataset, a deformed semicircle law appears. In this regime, we also calculate the asymptotic test and training errors for random feature regression. Joint work with Zhichao Wang (UCSD).