  Scattering often limits the controlled delivery of light in applications such
as biomedical imaging, optogenetics, optical trapping, and fiber-optic
communication or imaging. Such scattering can be controlled by appropriately
shaping the light wavefront entering the material. Here, we demonstrate a
machine-learning approach for light control. Using pairs of binary intensity
patterns and intensity measurements we train neural networks (NNs) to provide
the wavefront corrections necessary to shape the beam after the scatterer.
Additionally, we demonstrate that NNs can be used to find a functional
relationship between transmitted and reflected speckle patterns. As a proof of
the validity of this relationship, we demonstrate focusing and scanning of
light in transmission through opaque media using reflected light. Our approach
demonstrates the versatility of NNs for light shaping and for efficiently and
flexibly correcting for scattering. In particular, the feasibility of
transmission control based on reflected light opens up new opportunities for
applications in optical imaging, sensing, and light delivery.
