We study the first-order flocking transition of birds flying in
low-visibility conditions by employing three different representative types of
neural network (NN) based machine learning architectures that are trained via
either an unsupervised learning approach called "learning by confusion" or a
widely used supervised learning approach. We find that after the training via
either the unsupervised learning approach or the supervised learning one, all
of these three different representative types of NNs, namely, the
fully-connected NN, the convolutional NN, and the residual NN, are able to
successfully identify the first-order flocking transition point of this
nonequilibrium many-body system. This indicates that NN based machine learning
can be employed as a promising generic tool to investigate rich physics in
scenarios associated to first-order phase transitions and nonequilibrium
many-body systems.Comment: 7 pages, 3 figure