Recurrent Neural Networks (RNNs): A gentle Introduction and Overview
Abstract
The abstract provides an overview of key concepts in Recurrent Neural Networks, including backpropagation through time, Long Short-Term Memory Units, Attention Mechanism, and Pointer Networks, crucial for understanding recent advancements in language modeling, speech recognition, image descriptions, and video tagging.
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.
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