The recurrent neural network is used to handle sequential data, such as time-series data or text. The RNNs work way differently from traditional neural networks, such as having a memory to allow them to retain information about previous inputs and use it to make decisions about current inputs.
The loops within the network structure are incorporated by RNNs, allowing them to take the output of a previous step as an input to the current step. The feedback loop is created by allowing the network to retain information about previous inputs and use it to inform its decisions about current inputs.
The companies involved in using RNNs are:
- Google uses RNNs for various applications, such as speech recognition, machine translation, and image captioning. Google Translate is one of the most famous RNN-based products.
- Amazon uses RNNs for natural languages processing tasks such as sentiment analysis, chatbots, and personalized recommendations. Amazon Alexa is also one of the RNNs used in speech recognition and language understanding.
- Tesla uses RNNs for self-driving cars to help them understand the surrounding environment and make real-time-based decisions. RNNs are mainly used for such applications because they can handle the sequential nature of the data collected by sensors.
- Netflix uses RNNs for personalized recommendations and to predict what users might want to watch based on their viewing history and behavior.
- Microsoft uses RNNs for speech recognition, language modeling, and natural language processing in products such as Cortana, Skype, and Microsoft Translator.