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RNNs are powerful machine learning models and have found use in a wide range of areas. It is distinctly different from CNN models like GoogleNet. In this article, we have explored the different applications of RNNs in detail. The main focus of RNNs is to use sequential data.
RNNs are widely used in the following domains/ applications:
- Prediction problems
- Language Modelling and Generating Text
- Machine Translation
- Speech Recognition
- Generating Image Descriptions
- Video Tagging
- Text Summarization
- Call Center Analysis
- Face detection, OCR Applications as Image Recognition
- Other applications like Music composition
Prediction problems
RNNs are generally useful in working with sequence prediction problems. Sequence prediction problems come in many forms and are best described by the types of inputs and outputs it supports.
Sequence prediction problems include:
One-to-Many:In this type of problem, an observation is mapped as input to a sequence with multiple steps as an output.
Many-to-One: Here a sequence of multiple steps as input are mapped to a class or quantity prediction.
Many-to-Many: A sequence of multiple steps as input are mapped to a sequence with multiple steps as output.The Many-to-Many problem is often referred to as sequence-to-sequence, or seq2seq .
The problem with Recurrent neural networks was that they were traditionally difficult to train. The Long Short-Term Memory, or LSTM, network is one of the most successful RNN because it solves the problems of training a recurrent network and in turn has been used on a wide range of applications.RNNs and LSTMs have received the most success when working with sequences of words and paragraphs, generally in the field of natural language processing(NLP).They are also used as generative models that produce a sequence output, not only with text, but on applications such as generating handwriting.
Recurrent neural networks are not appropriate for tabular datasets such as those in a CSV file or spreadsheet. They are also not appropriate for datasets containing image data .
RNNs and LSTMs on being tested with time series forecasting problems, produced poor results. Autoregression methods, even linear methods often perform much better. Even simple MLPs applied on the same data performed better than LSTMs.
Following are some of the applications of RNNs.
Language Modelling and Generating Text
- Language Modelling and Generating Text
Taking a sequence of words as input, we try to predict the possibility of the next word. This can be considered to be one of the most useful approaches for translation since the most likely sentence would be the one that is correct.In this method, the probability of the output of a particular time-step is used to sample the words in the next iteration.
For more information on language modelling and text generation, you may read this paper
Machine Translation
- Machine Translation
RNNs in one form or the other can be used for translating text from one language to other . Almost all of the Translation systems being used today use some advanced version of a RNN. The input can be the source language and the output will be in the target language which the user wants.
Currently one of the most popular and prominent machine translation application is Google Translate.There are even numerous custom recurrent neural network applications used to refine and confine content by various platforms. ECommerce platforms like Flipkart, Amazon, and eBay make use of machine translation in many areas and it also helps with the efficiency of the search results.
For more information on Machine Translation using RNNs, read this paper
Speech Recognition
- Speech Recognition
RNNs can be used for predicting phonetic segments considering sound waves from a medium as an input source .The set of inputs consists of phoneme or acoustic signals from an audio which are processed in a proper manner and taken as inputs. The RNN network will compute the phonemes and then produce a phonetic segment along with the likelihood of output.The steps used in speech recognition are as follows:-
- The input data is first processed and recognized through a neural network. The result consists of a varied collection of input sound waves.
- The information contained in the sound wave is further classified by intent and through keuwords related to the query.
- Then input sound waves are classified into phonetic segments and are pieced together into cohesive words using a RNN application. The output consists of a pattern of phonetic segments put together into a singular whole in a logical manner.
Read this paper to read more about speech recognition using RNNs.
Generating Image Descriptions
- Generating Image Descriptions
A combination of CNNs and RNNs are used to provide a description of what exactly is happening inside an image. CNN does the segmentation part and RNN then uses the segmented data to recreate the description.
Click this paper to read more about generating image descriptions.
Video Tagging
- Video Tagging
RNNs can be used for video search where we can do image description of a video divided into numerous frames.
Text Summarization
- Text Summarization
This application can provide major help in summarizing content from literatures and customising them for delivery within applications which cannot support large volumes of text. For example, if a publisher wants to display the summary of one of his books on its backpage to help the readers get an idea of the content present within, Text Summarization would be helpful.
For more information on Text summarization based on RNNs read this (research paper)
Call Center Analysis
- Call Center Analysis
This can be considered as one of the major applications of RNNs in the field of audio processing. Customer metrics are measured on the output of the call rather than the call itself.However, analyzing the call itself will provide businesses with the solutions to why the support staff succeeded and what were the steps taken in resolving their customer issue. This learning can then be studied and reapplied to other similar scenarios or to train new support representatives. Hence the entire process can be automated based on the use of Recurrent Neural Networks to process and synthesize actual speech from the call for analysis purpose. Such synthesized speech can be further taken as an input to a tone analysis algorithm to measure the emotions and sentiments related to various parts of the conversation. This would help the business identify when the customer is satisfied with the service and support and when a customer has faced issues. Although these things can be done by involving simple human observations, automation will help business quantify the results and derive insights from them for future knowledge since all the output data would be stored in a database.
Face detection, OCR Applications as Image Recognition
- Face detection, OCR Applications as Image Recognition
Image recognition is one of the major applications of computer vision. It is also one of the most accessible form of RNN to explain.
In its core, the algorithm is designed to consider one unit of image as input and produce the description of the image in the form of multiple groups of output .
The image recognition framework includes:
convolutional neural network that processes the image and recognizes the features of the pictures,
recurrent neural networks that makes use of the known features to make sense of the image and put together a proper description of the input image.
There are numerous benifits of Image recognition in the field of business - it can be used as a streamlining tool to makes it easier for the customer to operate with the service, find relevant images, navigate through information, and make purchases and furthermore adding to the security of the customer. The most prominent industries that are making use of image recognition are Search engines, eCommerce, Social Media, Security and Networking.
Read this paper for more information regarding Image Recognition.
Other applications of RNNs
A few other applications of RNNs include:
- Time series anomaly detection
- Rhythm learning
- Music composition
- Handwriting recognition
- Grammar learning
- Human action recognition
- Protein Homology Detection
- Predicting subcellular localization of proteins
- Several prediction tasks in the area of business process management and Prediction in medical care pathways.
With this, you have the complete knowledge of where RNNs can be applied and how powerful it is.
