Recurrent Neural Networks (RNNs): A gentle Introduction and Overview

TL;DR

This paper provides a clear overview of Recurrent Neural Networks, explaining fundamental concepts and recent advances to help readers understand their role in language, speech, and image processing tasks.

Contribution

It offers a concise introduction to RNNs, covering essential and recent developments, aiding newcomers in grasping complex neural network concepts.

Findings

Clarifies key RNN concepts like Backpropagation through Time and LSTM

Introduces recent advances such as Attention Mechanism and Pointer Networks

Provides recommendations for further reading on complex topics

Abstract

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.