  We address the problem of analytic continuation of imaginary-frequency
Green's functions, which is crucial in many-body physics, using machine
learning based on a multi-level residual neural network. We specifically
address potential biases that can be introduced due to the use of artificially
created spectral functions that are employed to train the neural network. We
also implement an uncertainty estimation of the predicted spectral function,
based on Monte Carlo dropout, which allows to identify frequency regions where
the prediction might not be accurate, and we study the effect of noise, in
particular also for situations where the noise level during training is
different from that in the actual data. Our analysis demonstrates that this
method can indeed achieve a high quality of prediction, comparable or better
than the widely used maximum entropy method, but that further improvement is
currently limited by the lack of true data that can be used for training. We
also benchmark our approach by applying it to the case of SrVO$_3$, where an
accurate spectral function has been obtained from dynamical mean-field theory
using a solver that works directly on the real frequency axis.
