  Recent developments on a deep learning feed-forward network for estimating
elliptic flow ($v_2$) coefficients in heavy-ion collisions have shown us the
prediction power of this technique. The success of the model is mainly the
estimation of $v_2$ from final state particle kinematic information and
learning the centrality and the transverse momentum ($p_{\rm T}$) dependence of
$v_2$. The deep learning model is trained with Pb-Pb collisions at
$\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events simulated with a multiphase
transport model (AMPT). We extend this work to estimate $v_2$ for light-flavor
identified particles such as $\pi^{\pm}$, $\rm K^{\pm}$, and $\rm p+\bar{p}$ in
heavy-ion collisions at RHIC and LHC energies. The number of constituent quark
(NCQ) scaling is also shown. The evolution of $p_{\rm T}$-crossing point of
$v_2(p_{\rm T})$, depicting a change in meson-baryon elliptic flow at
intermediate-$p_{\rm T}$, is studied for various collision systems and
energies. The model is further evaluated by training it for different $p_{\rm
T}$ regions. These results are compared with the available experimental data
wherever possible.

High Energy Physics - Phenomenology;High Energy Physics - Experiment;Nuclear Experiment;Nuclear Theory