RNNs work by processing sequential data, where the output of each step depends on the input at the current step and the information from previous steps. Unlike feedforward networks, RNNs have a feedback loop that allows them to maintain a "memory" of prior inputs, making them suitable for time-series data, speech, or text.
During training, RNNs use backpropagation through time (BPTT) to compute gradients and update weights. However, standard RNNs struggle with long-term dependencies due to vanishing gradients. To address this, variants like LSTMs and GRUs introduce gating mechanisms that selectively remember or forget information, enabling them to handle long sequences effectively.
RNNs are widely used in applications such as language modeling, sentiment analysis, and machine translation. While powerful, they have been increasingly supplemented or replaced by Transformer models in tasks requiring long-range dependencies due to Transformers' efficiency and scalability.