  Is there any theoretical guarantee for the approximation ability of neural
networks? The answer to this question is the "Universal Approximation Theorem
for Neural Networks". This theorem states that a neural network is dense in a
certain function space under an appropriate setting. This paper is a
comprehensive explanation of the universal approximation theorem for
feedforward neural networks, its approximation rate problem (the relation
between the number of intermediate units and the approximation error), and
Barron space in Japanese.
