A recurrent neural network (RNN) is a type of neural network commonly used in speech recognition. RNNs are designed to recognize the sequential characteristics in data and use patterns to predict the next likely scenario. Unlike other neural networks, an RNN has an internal memory that enables it to remember historical input; this allows it to make decisions by considering current input alongside learning from previous input. In this way, an RNN can form a much deeper understanding of a sequence and its context than other types of deep learning algorithms, and therefore make more precise predictions.
Due to their precise predictive results, recurrent neural networks are the preferred algorithm for tasks such as speech recognition, language translation, financial forecasting, weather prediction, and image recognition. RNNs are the engines behind speech recognition applications such as Apple’s Siri and Google’s Voice Search, as well as chatbots and translation tools.