  In this paper we propose that artificial neural network, the basis of machine
learning, is useful to generate the inflationary landscape from a cosmological
point of view. Traditional numerical simulations of a global cosmic landscape
typically need an exponential complexity when the number of fields is large.
However, a basic application of artificial neural network could solve the
problem based on the universal approximation theorem of the multilayer
perceptron. A toy model in inflation with multiple light fields is investigated
numerically as an example of such an application.
