  An innovative \textit{deep learning} approach has been adopted to formulate
the eddy-viscosity for large eddy simulation (LES) of wall-bounded turbulent
flows. A deep neural network (DNN) is developed which learns to evaluate the
eddy-viscosity from a dataset generated for a channel flow at friction Reynolds
number $Re_{\tau} = 395$ using the Dynamic Smagorinsky subgrid-scale model.
Later this DNN is employed to predict the eddy-viscosity for a number of grid
configurations for channel flow at $Re_{\tau} = 395$ and $590$. The statistics
computed from the DNN based LES model show an excellent match with direct
numerical simulations (DNS). In some cases, particularly for the coarse grid
simulations, the DNN based model yields statistics closer to DNS than those
from the Dynamic Smagorinsky model. The turbulent kinetic energy budget terms
also manifest a satisfactory match with the DNS results. This model computes
eddy-viscosity $2-8$ times quicker than the Dynamic Smagorinsky model. This DNN
based LES model is also able to closely mimic the duct flow at $Re_{\tau} =
300$ in a qualitatively and quantitatively similar manner as the LES using the
Dynamic Smagorinsky model and a DNS from a previous study. This study
demonstrates the feasibility of deep learning for parameterizing the
subgrid-scales (SGS) in a turbulent flow accurately in a cost-effective manner.
In a broader perspective, deep learning based models can be a promising
alternative to traditional RANS and LES models for simulating complex turbulent
flows.
