  In this work, we map the phase diagrams of one-dimensional quasiperiodic
models using artificial neural networks. We observe that the multi-class
classifier precisely distinguishes the various phases, namely the delocalized,
multifractal, and localized phases, when trained on the eigenstates of the
long-range Aubry-Andr\'e Harper (LRH) model. Additionally, when this trained
multi-layer perceptron is fed with the eigenstates of the Aubry-Andr\'e Harper
(AAH) model, it identifies various phases with reasonable accuracy. We examine
the resulting phase diagrams produced using a single disorder realization and
demonstrate that they are consistent with those obtained from the conventional
method of fractal dimension analysis. Interestingly, when the neural network is
trained using the eigenstates of the AAH model, the resulting phase diagrams
for the LRH model are less exemplary than those previously obtained. Further,
we study binary classification by training the neural network on the
probability density corresponding to the delocalized and localized eigenstates
of the AAH model. We are able to pinpoint the critical transition point by
examining the metric ``accuracy" for the central eigenstate. The effectiveness
of the binary classifier in identifying a previously unknown multifractal phase
is then evaluated by applying it to the LRH model.
