  Recurrent neural networks (RNNs) are capable of learning features and long
term dependencies from sequential and time-series data. The RNNs have a stack
of non-linear units where at least one connection between units forms a
directed cycle. A well-trained RNN can model any dynamical system; however,
training RNNs is mostly plagued by issues in learning long-term dependencies.
In this paper, we present a survey on RNNs and several new advances for
newcomers and professionals in the field. The fundamentals and recent advances
are explained and the research challenges are introduced.
