Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are Computational models designed to process sequential data by maintaining a "memory" of previous inputs through feedback loops. Unlike Convolutional Neural Networks, which excel at spatial patterns, RNNs excel at temporal patterns—sequences where the order matters.

At their core, RNNs pass information forward through time: each neuron receives not only new input but also its own previous output, creating a chain of dependencies. This architecture makes them natural for Natural Language Processing, speech recognition, time-series forecasting, and any task where context accumulated over time is crucial.

However, RNNs face a notorious challenge: the "vanishing gradient problem," where signals decay as they propagate backward through many time steps, making it hard to learn long-range dependencies. Variants like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) solved this through gated mechanisms that carefully control information flow.

Modern Large Language Models largely replaced vanilla RNNs with Transformer architectures, which process sequences in parallel rather than sequentially. Yet RNNs remain foundational to understanding how neural networks handle time, and they remain practical for resource-constrained applications.

Related

Backpropagation, LSTM, Transformer, Sequence modeling, Gradient descent, Time series analysis