  We present a novel deep learning method for estimating time-dependent
parameters in Markov processes through discrete sampling. Departing from
conventional machine learning, our approach reframes parameter approximation as
an optimization problem using the maximum likelihood approach. Experimental
validation focuses on parameter estimation in multivariate regression and
stochastic differential equations (SDEs). Theoretical results show that the
real solution is close to SDE with parameters approximated using our neural
network-derived under specific conditions. Our work contributes to SDE-based
model parameter estimation, offering a versatile tool for diverse fields.
