  We develop a transferable machine learning model which predicts structural
relaxation from amorphous supercooled liquid structures. The trained networks
are able to predict dynamic heterogeneity across a broad range of temperatures
and time scales with excellent accuracy and transferability. We use the network
transferability to predict dynamic heterogeneity down to the experimental glass
transition temperature, $T_g$, where structural relaxation cannot be analyzed
using molecular dynamics simulations. The results indicate that the strength,
the geometry and the characteristic length scale of the dynamic heterogeneity
evolve much more slowly near $T_g$ compared to their evolution at higher
temperatures. Our results show that machine learning techniques can provide
physical insights on the nature of the glass transition that cannot be gained
using conventional simulation techniques.
