recurrent network in artificial intelligence

recurrent network in artificial intelligence

Recurrent Network:

Introduction Recurrent Neural Networks (RNN) is a powerful and robust type of neural networks and belong to the most promising algorithms out there at the moment because they are the only ones with an internal memory.

RNN's are relatively old, like many other deep learning algorithms. They were initially created in the 1980s, but can only show their real potential for a few years, because of the increase in available computational power, the massive amounts of data that we have nowadays and the invention of LSTM in the 1990s.

Because of their internal memory, RNN's are able to remember important things about the input they received, which enables them to be very precise in predicting what's coming next.

This is the reason why they are the preferred algorithm for sequential data like time series, speech, text, financial data, audio, video, weather and much more because they can form a much deeper understanding of a sequence and its context, compared to other algorithms.

Recurrent Neural Networks produce predictive results in sequential data that other algorithms can't. But when do you need to use a Recurrent Neural Network?