  Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) are one of the
most powerful dynamic classifiers publicly known. The network itself and the
related learning algorithms are reasonably well documented to get an idea how
it works. This paper will shed more light into understanding how LSTM-RNNs
evolved and why they work impressively well, focusing on the early,
ground-breaking publications. We significantly improved documentation and fixed
a number of errors and inconsistencies that accumulated in previous
publications. To support understanding we as well revised and unified the
notation used.
