A Recurrent Neural Network (RNN) is a type of artificial neural network designed specifically for processing sequential data, such as time series, speech, text, or any data where the order of elements is essential. Unlike traditional neural networks, which treat each input as independent, RNNs have connections that loop back on themselves. This looping structure allows RNNs to maintain a form of “memory,” where information from previous inputs in the sequence can influence the network’s response to the current input. In essence, RNNs are particularly suited to tasks where the context from earlier steps is crucial for understanding later steps.

Imagine reading a sentence word by word; the meaning of each word is influenced by the ones that came before it. RNNs replicate this by passing information through a hidden state that gets updated at each step in the sequence. However, standard RNNs can struggle to remember information over long sequences, leading to a challenge known as the “vanishing gradient problem,” where crucial information fades over time. To address this, researchers developed advanced forms of RNNs, like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). These enhanced models incorporate mechanisms to retain and forget information selectively, improving the network’s ability to work with longer sequences.

RNNs are widely used in applications involving language modeling, like speech recognition, translation, and text generation, as well as in predicting stock prices or analyzing medical records over time. They learn from examples and become better at predicting patterns by adjusting weights within the network. Despite their capabilities, RNNs are gradually being complemented or even replaced in some areas by Transformer models, which excel at handling long-term dependencies without the same limitations.