  The present study investigates the non-Oberbeck-Boussinesq (NOB) effects
which arise due to the temperature dependence of material properties in
cryogenic helium experiments of turbulent Rayleigh-B\'enard convection. They
are manifest as a difference of the measured mean temperature at the center of
the closed cell, $T_c$, from the arithmetic mean temperature obtained from the
prescribed fixed and uniform temperatures at the top and bottom copper plates
of the apparatus, $T_m = (T_{bot} +T_{top})=2$. Therefore, the material
properties such as specific heat at constant pressure, dynamic viscosity,
thermal conductivity, the isobaric expansivity, and the mass density are
expanded into power series with respect to temperature up to the quadratic
order with coeffcients obtained from the software package HEPAK. A subsequent
nonlinear regression that uses deep convolutional networks delivers a
dependence of the strength of non-Oberbeck-Boussinesq effects in the
pressure-temperature parameter plane. Strength of the NOB effects is evaluated
via the deviation of the mean temperature profile $\xi_{NOB} = T_m - T_c$ from
the top/bottom-symmetric Oberbeck-Boussinesq case $\xi_{NOB} = 0$. Training
data for the regression task are obtained from 236 individual long-term
laboratory measurements at different Rayleigh numbers which span 8 orders of
magnitude.
