How do recurrent neural networks (RNNs) work?

Recurrent Neural Networks (RNNs) are a type of neural network designed to process sequential data by maintaining a hidden state that captures information from previous steps in the sequence. Unlike traditional feedforward neural networks, which process inputs independently, RNNs use loops to pass information from one step to the next. This makes them well-suited for tasks like time series analysis, natural language processing, or speech recognition, where the order of inputs matters. For example, in text processing, an RNN can analyze a sentence word by word, using context from earlier words to interpret later ones.

RNNs work by iterating through each element in a sequence and updating a hidden state vector at each step. At each timestep, the network takes two inputs: the current data point (e.g., a word in a sentence) and the hidden state from the previous step. These inputs are combined using weights and activation functions (like tanh or ReLU) to produce a new hidden state and an optional output. The key feature is that the same set of weights is reused at every step, allowing the network to generalize across sequences of varying lengths. For instance, when predicting the next word in a sentence, the hidden state might track grammatical structure or topic context. However, basic RNNs struggle with long-term dependencies due to the vanishing gradient problem, where gradients shrink exponentially during backpropagation, making it hard to learn relationships between distant steps.

To address these limitations, variants like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks were developed. LSTMs introduce gates that regulate the flow of information, selectively remembering or forgetting past states. GRUs simplify this with fewer gates while retaining similar benefits. These architectures are widely used in applications like machine translation (e.g., Google Translate) or speech-to-text systems. For developers, frameworks like TensorFlow or PyTorch provide built-in RNN layers. For example, in Keras, a simple RNN can be implemented with SimpleRNN, LSTM, or GRU layers. Despite the rise of Transformer models, RNNs remain relevant for tasks requiring lightweight sequential processing or when training data is limited. Their ability to handle variable-length inputs and model temporal dynamics makes them a practical tool in many scenarios.