  The process of training an artificial neural network involves iteratively
adapting its parameters so as to minimize the error of the network's
prediction, when confronted with a learning task. This iterative change can be
naturally interpreted as a trajectory in network space -- a time series of
networks -- and thus the training algorithm (e.g. gradient descent optimization
of a suitable loss function) can be interpreted as a dynamical system in graph
space. In order to illustrate this interpretation, here we study the dynamical
properties of this process by analyzing through this lens the network
trajectories of a shallow neural network, and its evolution through learning a
simple classification task. We systematically consider different ranges of the
learning rate and explore both the dynamical and orbital stability of the
resulting network trajectories, finding hints of regular and chaotic behavior
depending on the learning rate regime. Our findings are put in contrast to
common wisdom on convergence properties of neural networks and dynamical
systems theory. This work also contributes to the cross-fertilization of ideas
between dynamical systems theory, network theory and machine learning
