Sequential Models (RNN, LSTM, GRU) in AI & Artificial Intelligence

A Recurrent Neural Network (RNN) is unique because it contains a feedback loop. At each time step, the model takes a word and its own Hidden State from the previous step. This creates a form of short-term memory, allowing the network to process variable-length sequences. However, because gradients are multiplied repeatedly during training, the influence of early words diminishes exponentially—the infamous Vanishing Gradient problem.

The LSTM (Long Short-Term Memory) network introduced a sophisticated 'conveyor belt' called the Cell State. Controlled by three gates—Forget, Input, and Output—the LSTM can selectively store or delete information over hundreds of time steps. This breakthrough allowed AI to maintain context across long paragraphs, making deep sentiment analysis and complex translation possible.

As a more modern alternative, the GRU (Gated Recurrent Unit) simplifies the LSTM architecture by merging the cell state and hidden state. With fewer parameters to train, GRUs are faster and often achieve comparable performance on smaller datasets. While both are powerful, they process text sequentially (one word at a time), which eventually led to their replacement by the parallelized Transformer architecture.