Having examined the theoretical foundations of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, including their internal gating structures ($f_t, i_t, o_t$ in LSTMs, $z_t, r_t$ in GRUs) aimed at managing information flow and mitigating gradient issues found in simple RNNs, we now proceed to their practical implementation.

This chapter focuses on translating theory into functional code using contemporary deep learning frameworks. You will learn to instantiate LSTM and GRU layers provided by library APIs, configure their key parameters (like hidden unit count and activation choices), and handle the expected 3D tensor shapes (batch size, time steps, features) for inputs and outputs. We will also construct more sophisticated models by stacking recurrent layers for increased representational capacity and implementing bidirectional processing, allowing the network to consider context from both forward and backward passes through the sequence. A practical coding example will consolidate these concepts by applying an LSTM or GRU model to a sentiment analysis task.