Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Robin M. Schmidt

TL;DR

This paper provides a concise, concept-driven overview of recurrent neural networks and their key innovations.It surveys foundational ideas (BPTT, vanishing/exploding gradients, LSTMs, DRNNs, BRNNs) and advances (encoder-decoder/seq2seq, attention, Transformer, Pointer Networks) with guiding equations and diagrams.By connecting formal mechanisms to practical considerations (e.g., truncated BPTT, attention scores, positional encodings), it offers a foundational roadmap for researchers and practitioners to engage with current and future sequence-modeling work.The work emphasizes how these architectures enable scalable sequence processing across domains such as language, speech, and planning tasks, illustrating broader impact through references and practical examples.

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.

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

TL;DR

This paper provides a concise, concept-driven overview of recurrent neural networks and their key innovations.It surveys foundational ideas (BPTT, vanishing/exploding gradients, LSTMs, DRNNs, BRNNs) and advances (encoder-decoder/seq2seq, attention, Transformer, Pointer Networks) with guiding equations and diagrams.By connecting formal mechanisms to practical considerations (e.g., truncated BPTT, attention scores, positional encodings), it offers a foundational roadmap for researchers and practitioners to engage with current and future sequence-modeling work.The work emphasizes how these architectures enable scalable sequence processing across domains such as language, speech, and planning tasks, illustrating broader impact through references and practical examples.

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.

Paper Structure

This paper contains 16 sections, 29 equations, 18 figures, 1 table.

Table of Contents

  1. Introduction & Notation
  2. Backpropagation Through Time (BPTT) & Truncated BPTT
  3. Problems of RNNs: Vanishing & Exploding Gradients
  4. Long Short-Term Memory Units (LSTMs)
  5. Deep Recurrent Neural Networks (DRNNs)
  6. Bidirectional Recurrent Neural Networks (BRNNs)
  7. Encoder-Decoder Architecture & Sequence to Sequence (seq2seq)
  8. Attention Mechanism & Transformer
  9. Definition
  10. Different types of score functions
  11. Transformer
  12. Pointer Networks (Ptr-Nets)
  13. Conlusion & Outlook
  14. Visual Representation of LSTMs
  15. Visual Representation of seq2seq with Attention
  16. ...and 1 more sections

Figures (18)

  • Figure 1: Visualisation of differences between Feedfoward NNs und Recurrent NNs
  • Figure 2: Architecture of a bidirectional recurrent neural network
  • Figure 3: Encoder-Decoder Architecture Overview alternated from: zhang2019dive
  • Figure 4: Visualisation of the Sequence to Sequence (seq2seq) Model
  • Figure 5: Example of an Alignment matrix of "L'accord sur la zone économique européen a été signé en août 1992" (French) and its English translation "The agreement on the European Economic Area was signed in August 1992": attention1
  • ...and 13 more figures