  In this review paper, we give a comprehensive overview of the large variety
of approximation results for neural networks. Approximation rates for classical
function spaces as well as benefits of deep neural networks over shallow ones
for specifically structured function classes are discussed. While the mainbody
of existing results is for general feedforward architectures, we also depict
approximation results for convolutional, residual and recurrent neural
networks.
