  We demonstrate that an image recognition algorithm based on a convolutional
neural network provides a powerful procedure to differentiate between ergodic,
non-ergodic extended (fractal) and localized phases in various systems:
single-particle models, including random-matrix and random-graph models, and
many-body quantum systems. The network can be successfully trained on a small
data set of only 500 wave functions (images) per class for a single model. The
trained network can then be used to classify phases in the other models and is
thus very efficient. We discuss the strengths and limitations of the approach.
