  We study the phase transitions of three-dimensional (3D) classical O(3) model
and the two-dimensional (2D) classical XY model, as well as both the quantum
phase transitions of 2D and 3D dimerized spin-1/2 antiferromagnets, using the
techniques of supervised neural network (NN). Moreover, unlike the conventional
approaches commonly used in the literature, the training sets employed in our
investigation are neither the theoretical nor the real configurations of the
considered systems. Remarkably, with such an unconventional set up of the
training stage in conjunction with semi-experimental finite-size scaling
formulas, the associated critical points determined by the NN method agree well
with the established results in the literature. The outcomes obtained here
imply that certain unconventional training strategies, like the one used in
this study, are not only cost-effective in computation, but are also applicable
for a wild range of physical systems.
