To understand the innovations of the Transformer architecture, it's helpful to first examine the sequence processing models that came before it. This chapter provides a concise review of Recurrent Neural Networks (RNNs) and their more sophisticated variants, Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs).

We will look at:

• The core idea of sequential processing using hidden states in RNNs.
• The challenges faced by simple RNNs, such as the difficulty in capturing long-range dependencies due to vanishing gradients.
• How the gating mechanisms in LSTMs and GRUs were designed to mitigate these issues.
• The application of these recurrent architectures in sequence-to-sequence (seq2seq) tasks.

This review establishes the context for why attention mechanisms and the Transformer architecture represented a significant shift in modeling sequential data, which we will cover in the subsequent chapter.