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Table of contents
1.
Introduction
2.
Working of Recurrent Neural Network(RNN)
3.
Types of RNN’s
4.
Advantages of Recurrent Neural Network(RNN)
5.
Disadvantages of Recurrent Neural Network(RNN)
6.
Applications of Recurrent Neural Networks(RNN)
7.
Frequently Asked Questions
8.
Key Takeaways
Last Updated: Mar 27, 2024

Recurrent Neural Network - Intro

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Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

We use a lot of voice assistants daily like Alexa, Siri, Google Assistant. These voice assistants are interactable, easy to use. We have indirectly depended on voice assistants to set reminders, wake up calls, automatic lights. The algorithm behind these voice assistants is the Recurrent Neural Network(RNN).

 

Source

 

In this blog let’s discuss the working of the Recurrent Neural Network(RNN), types of Recurrent Neural Network followed by advantages and disadvantages. Let’s explore different applications of the Recurrent Neural Network(RNN).

Also Read, Resnet 50 Architecture

Working of Recurrent Neural Network(RNN)

RNNs are a powerful and robust type of neural network and belong to the most promising algorithms in use because it is the only one with internal memory.

Recurrent Neural Networks(RNN) were initially created in the 1980s, but only in recent years have we seen their true potential. An increase in computational power along with the massive amounts of data that we now have to work with and the invention of long short-term memory (LSTM) in the 1990s, has brought RNNs to the foreground.

RNN has vast internal memory that helps to remember important information of inputs received, which allows in predicting the next outputs. So preferred algorithm for sequential data like time series, speech, text, financial data, audio, video, weather, and much more. Recurrent neural networks(RNN) can form a much deeper understanding of a sequence and its context compared to other algorithms.

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From the above image let’s understand how an RNN works. First, we need to pass an input layer, that will pass through different hidden layers and process the outputs. So what is different in hidden layers of other neural networks and an RNN, because it has two inputs present and recent past that helps to predict the outputs. 

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Types of RNN’s

In general, Recurrent Neural Networks are of four types the name reflects the same as types of functions names. They are:

  1. One to One
  2. One to Many
  3. Many to One
  4. Many to Many

We use one to many, many to many (translation) and many to one (classifying a voice). Many to many and many to one are most commonly used for voice assistants and most helpful in recurrent neural networks(RNN).

Advantages of Recurrent Neural Network(RNN)

Recurrent Neural Networks is one of the most efficient algorithms. So it has many vast advantages:

  • RNN can process the input of any length.
  • Even if the input size is larger, the model size does not increase.
  • An RNN model is modeled to remember each information throughout the time which is very helpful in any time series predictor.
  • The weights can be shared across the time steps.
  • RNN can use their internal memory for processing the arbitrary series of inputs which is not the case with feedforward neural networks.

Disadvantages of Recurrent Neural Network(RNN)

  • Due to its recurrent nature, the computation is slow.
  • Training of RNN models can be difficult.
  • If we are using relu or tanh as activation functions, it becomes very difficult to process sequences that are very long.
  • Prone to problems such as exploding and gradient vanishing.

Applications of Recurrent Neural Networks(RNN)

RNN is one of the most used efficient algorithms for different applications like:

  • Predicting Problems
  • Time Series
  • Machine Translation
  • Speech Recognition
  • Language Modelling
  • Generation of Text
  • Video tagging
  • Text Summarization
  • Call Centre Analysis
  • Generating Image Descriptions

Frequently Asked Questions

  1. What is RNN used for?
    Ans. Recurrent Neural Networks(RNN) are a type of Neural Network where the output from the previous step is fed as input to the current step. RNN's are mainly used for, Sequence Classification — Sentiment Classification & Video Classification. Sequence Labelling — Part of speech tagging & Named entity recognition.
     
  2. What is an RNN example?
    Ans. The most commonly used RNN is voice recognition, Time series, etc.
     
  3. What is a simple RNN?
    Ans. SimpleRNN, a fully-connected RNN where the output from the previous time step is to be fed to next timestep.
     
  4. What is Illustrator RNN?
    Ans. A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed or undirected graph along a temporal sequence.

Key Takeaways

In this blog we discussed

  • Introduction to Recurrent Neural Network(RNN)
  • Working
  • Types of RNN’s
  • Advantages and disadvantages
  • Applications

Check out this article - Padding In Convolutional Neural Network


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