Title: The effect of the choice of neural network depth and breadth on the size of its hypothesis space,
Abstract: We show that the number of unique function mappings in a neural network
hypothesis space is inversely proportional to $\prod_lU_l!$, where $U_{l}$ is
the number of neurons in the hidden layer $l$.