  We study the finite temperature (FT) phase transitions of two-dimensional
(2D) $q$-states Potts models on the square lattice, using the first principles
Monte Carlo (MC) simulations as well as the techniques of neural networks (NN).
We demonstrate that the ideas from NN can be adopted to study these considered
FT phase transitions efficiently. In particular, even with a simple NN
constructed in this investigation, we are able to obtain the relevant
information of the nature of these FT phase transitions, namely whether they
are first order or second order. Our results strengthens the potential
applicability of machine learning in studying various states of matters.
Subtlety of applying NN techniques to investigate many-body systems is briefly
discussed as well.
