Recurrent neural networks (RNNs) are designed specifically to process sequential data by maintaining a memory of previous inputs. Unlike traditional neural networks that treat each input independently, RNNs use loops in their architecture to pass information from one step to the next. This unique feature enables RNNs to keep track of earlier inputs in the sequence, which is crucial for tasks where context matters, such as time series analysis, natural language processing, and speech recognition.
The core idea behind RNNs is their use of a hidden state, which captures information about the sequence as it processes each new element. When an input is received, the RNN updates this hidden state based on the current input and the previous hidden state. For instance, when processing a sentence word by word, the RNN updates its memory with each word it reads, allowing it to understand context and relationships between words. This mechanism allows RNNs to generate outputs that take into account the entire sequence rather than just the most recent input.
However, standard RNNs can struggle with long sequences due to the vanishing gradient problem, where earlier information may become diluted as it propagates through many time steps. To address this, variations such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) have been developed. These architectures include special gating mechanisms to better retain important information and forget less relevant data over longer sequences. In practical terms, when building applications like chatbots or language translation systems, using LSTMs or GRUs can significantly improve performance, as they effectively capture the necessary context from past inputs without losing critical information over time.