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  • Amruta Bhaskar
  • Dec 26, 2019
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What are Recurrent Neural Networks?

A Recurrent Neural Network is a type of Neural Network where there exists a connection between the nodes along a temporal sequence. This connection is that of a directed graph. By temporal, we mean data that transitions with time. 

RNN Example – time-series data get the information of prices of stock prices that change with time, sensor technology readings, medical domains records, etc. These recurrent neural networks use their internal state or memory to process the sequence of input data. Such input is dependent upon the previous input. Therefore, there is a connection between the input sequences. Therefore, we use them in areas like natural language processing and speech recognition.

Why RNN?

Traditional neural networks lack the ability to address future inputs based on the ones in the past. For example, a traditional neural network will not be able to predict the next word in the sequence pattern based on the results of previous sequences, but as we know that recurrent neural network (RNN) most definitely can. Recurrent Neural networks, as the name suggests are recurring. finally, they get executed in forms of loops allowing this information to get persist.

Applications of Recurrent Neural Networks

This is the most amazing part of our Recurrent Neural Networks Tutorial. Below are some of the stunning applications of RNN, have a look –

1. Machine Translation

We make use of Recurrent Neural Networks in the translation engines to translate the text from one language to the other. They can do this with the combination of other models like LSTMs.


2. Speech Recognition

Recurrent Neural Networks have replaced the traditional speech recognition models that made use of Hidden Markov Models. As we know that Recurrent Neural Networks along with LSTMs it look much more better poised so that we can classifying speeches and converting them into text or words or sentences without any loss of context.


3. Automatic Image Tagger

Recurrent Neural network in conjunction with CNN (Convolution Neural Networks)will be able to detect the pictures or images and it will provide their information or description in the form of tags. For example, an image of a fox jumping over the fence is better explained appropriately using RNNs.


4. Sentiment Analysis

For understanding the sentiment of the user, we make use of sentiment analysis to mine positivity, negativity or the neutrality of the sentence. Therefore, RNNs are most adept at handling sequential data in order to find sentiments of the sentence.



Author: Ravinder Joshi

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