  This paper studies the problem of linear precoding for multiple-input
multiple-output (MIMO) communication channels employing finite-alphabet
signaling. Existing solutions typically suffer from high computational
complexity due to costly computations of the constellation-constrained mutual
information. In contrast to existing works, this paper takes a different path
of tackling the MIMO precoding problem. Namely, a data-driven approach, based
on deep learning, is proposed. In the offline training phase, a deep neural
network learns the optimal solution on a set of MIMO channel matrices. This
allows the reduction of the computational complexity of the precoder
optimization in the online inference phase. Numerical results demonstrate the
efficiency of the proposed solution vis-\`a-vis existing precoding algorithms
in terms of significantly reduced complexity and close-to-optimal performance.
