A recurrent neural network (RNN) is a type of neural network designed to process sequential data by maintaining a “memory” of previous inputs. Unlike standard feedforward networks, which process each input independently, RNNs use loops to pass information from one step in the sequence to the next. This makes them particularly useful for tasks where the order of data matters, such as time series analysis, natural language processing (NLP), or speech recognition. For example, when predicting the next word in a sentence, an RNN considers the words that came before it, updating an internal “hidden state” at each step to capture context.
RNNs are commonly applied to tasks involving sequences of varying lengths. For instance, in machine translation, an RNN might process an input sentence one word at a time, encode it into a hidden state, and then generate a translated sentence step by step. However, traditional RNNs struggle with long-term dependencies due to the “vanishing gradient” problem, where gradients (used to update the network during training) become too small to affect earlier parts of the sequence. To address this, variants like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) were developed. LSTMs use gates to control the flow of information, allowing them to retain important context over longer sequences. GRUs simplify this concept with fewer gates, balancing efficiency and performance.
For developers, implementing an RNN typically involves frameworks like TensorFlow or PyTorch, which provide prebuilt layers. For example, a simple RNN layer in PyTorch might be defined as torch.nn.RNN(input_size, hidden_size, num_layers), where input_size is the dimension of each input element (e.g., a word embedding), and hidden_size defines the memory capacity. Training requires sequences to be processed stepwise, often using backpropagation through time (BPTT), a variant of backpropagation tailored for sequences. Practical considerations include handling variable-length sequences (via padding or masking) and managing computational efficiency, as RNNs can be slow for very long sequences. Despite the rise of transformer-based models, RNNs remain relevant for lightweight tasks or scenarios where sequential processing is inherently intuitive, such as real-time sensor data analysis.