5. Sequence Models Understand Time and Context

RNNs, LSTMs, and GRUs are introduced for time series forecasting and NLP. These architectures retain memory over time, allowing for predictions that reflect context. Géron walks through how to implement them, train with teacher forcing, and optimize sequences using embeddings. He explores sequence-to-sequence models with attention and shows how language models are built using real-world datasets. The insight is that sequential learning captures dependencies and structure — and it requires careful management of vanishing gradients and computational resources.