Recurrent Networks

Published:

Recurrent networks (RNNs) are built for situations where information arrives in a sequence and the order actually matters. Think of reading a sentence: each new word only makes sense if you remember the ones before it. RNNs work the same way. Instead of processing every input as if it’s unrelated, they carry a small internal memory (a hidden state) from one step to the next. This lets the model use past information when predicting the next word in text, the next number in a time series, or the next sound in an audio signal.

As researchers pushed RNNs further, they ran into issues like models forgetting information too quickly or getting overwhelmed by long sequences. This led to improved designs such as LSTMs and GRUs, which handle longer contexts more reliably. RNNs became the backbone of early breakthroughs in language modeling and speech recognition.

Follow us on Facebook and LinkedIn to keep abreast of our latest news and articles