  An autoencoder (AE) and a generative adversarial networks (GANs) are trained
only once on a one-dimensional (1D) lattice of 200 sites. Moreover, the AE
contains only one hidden layer consisting of two neurons and both the generator
and the discriminator of the GANs are made up of two neurons as well. The
training set employed to train both the considered unsupervised neural networks
(NN) is composed of two artificial configurations. Remarkably, despite their
simple architectures, both the built AE and GANs have precisely determined the
critical points of several models, including the three-dimensional (3D)
classical $O(3)$ model, the two-dimensional (2D) generalized classical XY
model, the 2D two-state Potts model, and the 1D Bose-Hubbard model. The results
presented here as well as that shown in {\it Eur. Phys. J. Plus {\bf 136}, 1116
(2021)} suggest that when phase transitions are considered, an elegant
universal neural network that is extremely efficient and is applicable to broad
physical systems can be constructed with ease. In particular, since a NN
trained with two configurations can be applied to many models, it is likely
that when machine learning is concerned, the majority of phase transitions
belong to a class having two elements, i.e. the Ising class.
