Recurrent Neural Networks or RNN’s are commonly called is a powerful algorithm and contain a unique distinction. Of all the algorithms prevailing in the market, this is the only one with an internal memory.

They are highly recommended for complicated problems containing chronological data. Guess what? Our favorite Apple Siri and Google’s voice search assistant use this algorithm to carry out their day-to-day tasks.

HISTORY OF RECURRENT NEURAL NETWORKS

RNN’s were invented in the 1980’s and their importance increases manifold after the introduction of Long-Short-Term-Memory (LSTM) in the 1990’s. With huge sets of data we have to work with, this requires a tremendous increase in speed of computational processing.

Because of the important trait of having a memory inside them, they are the first choice of data scientists to process information like financial reports, weather forecast, audio, video etc.

HOW RECURRENT NEURAL NETWORKS FUNCTION

As mentioned above, RNN’s are used to process chronological data. So in order to understand them, you need a sound knowledge about feed forward neural networks and chronological data.

Let’s draw a comparison between feed forward neural networks and recurrent neural networks.

Feed forward neural networks are able to process information in a single direction only. They don’t have their own memory and are unable to predict accurate outcome. It only relies on the current input.

RNN’s on the other hand are able to predict outcomes accurately as they take into account data from current input and the past inputs as well.