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We show that the number of unique function mappings in a neural networkhypothesis space is inversely proportional to $\prod_lU_l!$, where $U_{l}$ isthe number of neurons in the hidden layer $l$.

The effect of the choice of neural network depth and breadth on the size of its hypothesis space