Previous chapters examined text representation methods like TF-IDF, which often disregard the order of words. Yet, language is inherently sequential; the arrangement of words carries significant meaning. This chapter focuses on models designed to process data where order matters.

We will start with the fundamentals of Recurrent Neural Networks (RNNs), explaining how they maintain state to process sequences. You will understand a common difficulty in training RNNs, known as the vanishing gradient problem. Following this, we will look at more sophisticated architectures developed to address this limitation: Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). We will cover their core mechanisms and conclude by discussing how these sequence-aware models can be applied to various natural language tasks, along with a practical implementation exercise.