Recurrent neural networks are also known as RNNs. This artificial neural network can process sequential data, recognize patterns, and predict outcomes.

Recurrent neural networks can perform the same task or operation repeatedly based on a sequence of inputs. RNNs have an internal memory that allows them to remember information about the input they receive to gain context.

RNNs are excellent for processing sequential data, such as time series. The correlation between the previous and next input cannot be sorted with CNNs or feed-forward neural networks.

Apple's Siri and Google's voice search use RNNs to process user input and predict results.

How do Recurrent Neural Networks work?

It is possible to consider Recurrent Neural Networks as linked networks. As a result, they can be used for speech recognition, language translation, etc., due to their chain-like architecture.

Depending on the design, an RNN may work with sequences of vectors as inputs, outputs, or both. An example of a sequenced input would be to take a sentence as an input and output a positive or negative sentiment value.

In addition, a sequenced output may take an image as input and produce a sentence as output.

Common Use Cases of Recurrent Neural Networks

Machine Translation: Deep learning models can be created using RNN to translate a text from one language into another without human intervention. In the case of translating a text from your native language into English, for example, you can do so.

Text Generation: RNNs can also generate text using deep learning. Using a series of previous words/characters in the text, a trained model determines the likelihood of an occurrence of a given word/character.

Image Captioning: Captioning is the process of creating text that describes the content of an image. Images can show the object and its action as well as the content of the image.

Speech Recognition: Automatic Speech Recognition (ASR), also known as speech recognition software, converts human speech into written or textual form. The main difference between speech recognition and voice recognition is that speech recognition converts voice data into text, while voice recognition identifies the user's voice.

Time-Series Forecasting: RNNs can be used to create time series prediction models after training from historical time-stamped data. The stock market is an excellent example. Stock market data can be used to construct a machine learning model that can predict the future stock price by learning what the model learns from the historical data. Investors can make data-driven investment decisions with this information.

Conclusion

An RNN is a recurrent neural network. It is an artificial neural network that can recognize patterns, process sequential data, and predict outcomes. A Recurrent Neural Network can perform the same operation on a sequence of inputs.

Recurrent neural networks consist of several networks that are connected. Machine Translation, Text Generation, Image Captioning, Speech Recognition, and Time-Series Forecasting are some of the applications of recurrent neural networks.