How RNNs Work — Remembering Previous States in Sequential Data

A normal neural network treats each input mostly as a fixed snapshot. But many problems are not snapshots. Text, speech, and time-series data depend on order. That is why RNNs exist. Core Idea A Recurrent Neural Network is designed for sequential data. It does not only look at the current input. It also carries information from previous steps. That carried information is called the hidden state. So an RNN can process a sequence one step at a time while keeping memory of what came before.