  We describe a method to obtain point and dispersion estimates for the
energies of jets arising from b quarks produced in proton-proton collisions at
an energy of $\sqrt{s} =$ 13 TeV at the CERN LHC. The algorithm is trained on a
large simulated sample of b jets and validated on data recorded by the CMS
detector in 2017 corresponding to an integrated luminosity of 41 fb$^{-1}$. A
multivariate regression algorithm based on a deep feed-forward neural network
employs jet composition and shape information, and the properties of
reconstructed secondary vertices associated with the jet. The results of the
algorithm are used to improve the sensitivity of analyses that make use of b
jets in the final state, such as the observation of Higgs boson decay to
$\mathrm{b\bar{b}}$.
