Summary  
This chapter demonstrates how to implement and compare LSTM and GRU recurrent neural network architectures in code by configuring their gating mechanisms, initializing hidden states, training over multiple epochs, printing training times and loss metrics, and evaluating their performance.  

General domain of usage  
Time series forecasting

A comparison is presented highlighting the key differences between **long short-term memory (LSTM)** and **gated recurrent unit (GRU)** networks.


Focus exclusively on Recurrent Neural Networks (RNNs) and their application to sequence generation. This advanced module covers the core theory of RNNs and practical approaches to modeling sequential data, preparing you for hands-on work with LSTM and GRU networks in Python.

LSTM vs GRU for Time Series