  Far-from-equilibrium many-body systems, from soap bubbles to suspensions to
polymers, learn the drives that push them. This learning has been observed via
thermodynamic properties, such as work absorption and strain. We move beyond
these macroscopic properties that were first defined for equilibrium contexts:
We quantify statistical mechanical learning with machine learning. Our toolkit
relies on a structural parallel that we identify between far-from-equilibrium
statistical mechanics and representation learning, which is undergone by neural
networks that contain bottlenecks, including variational autoencoders. We train
a variational autoencoder, via unsupervised learning, on configurations assumed
by a many-body system during strong driving. We analyze the neural network's
bottleneck to measure the many-body system's classification ability, memory
capacity, discrimination ability, and novelty detection. Numerical simulations
of a spin glass illustrate our technique. This toolkit exposes
self-organization that eludes detection by thermodynamic measures, more
reliably and more precisely identifying and quantifying learning by matter.
