  We propose a novel deep learning tool in order to study the evolution of dark
energy models. The aim is to combine two architectures: the Recurrent Neural
Networks (RNN) and the Bayesian Neural Networks (BNN), we named this full
network as RNN+BNN. The first one is capable of learning complex sequential
information to classify objects like supernovae and use the light-curves
directly to learn information from the sequence of observations. Since RNN is
not capable to calculate the uncertainties, BNN emerges as a solution for
problems in deep learning like, for example, the overfitting. For the trainings
we use measurements of the distance modulus $\mu(z)$, such as those provided by
Pantheon Supernovae Type Ia. In view of our results, the reported approach
turns out to be a first promising step on how we can train a new neural network
that can compute their own confidence regions for specific cosmological data.
It is worth stressing that the new technique allows to reduce the computational
load of expensive codes for dark energy models and probe the necessity of
modified dark energy models at large redshifts for a supernovae trained
sampler.
