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Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks

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arxiv 1909.09586 v1 pith:C5L6G4HG submitted 2019-09-12 cs.NE cs.CLcs.LG

Ralf C. Staudemeyer , Eric Rothstein Morris This is my paper · ORCID
classification cs.NEcs.CLcs.LG
keywords understandingwelllongmemorynetworksneuralpublicationsrecurrent
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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