Recurrent Neural Networks (RNNs): A gentle Introduction and Overview
It serves as an educational resource for those needing to grasp RNN basics to keep up with recent publications in fields like natural language processing and computer vision, but it is incremental as it primarily reviews existing knowledge.
This paper provides an introduction and overview of Recurrent Neural Networks (RNNs), explaining fundamental concepts like Backpropagation through Time and Long Short-Term Memory Units, as well as recent advances such as the Attention Mechanism and Pointer Networks, to help readers understand their applications in areas like language modeling and speech recognition.
State-of-the-art solutions in the areas of "Language Modelling & Generating Text", "Speech Recognition", "Generating Image Descriptions" or "Video Tagging" have been using Recurrent Neural Networks as the foundation for their approaches. Understanding the underlying concepts is therefore of tremendous importance if we want to keep up with recent or upcoming publications in those areas. In this work we give a short overview over some of the most important concepts in the realm of Recurrent Neural Networks which enables readers to easily understand the fundamentals such as but not limited to "Backpropagation through Time" or "Long Short-Term Memory Units" as well as some of the more recent advances like the "Attention Mechanism" or "Pointer Networks". We also give recommendations for further reading regarding more complex topics where it is necessary.