Summary  
This chapter demonstrates how to implement LSTM and GRU recurrent neural networks in code—defining their architectures, setting hyperparameters, running training loops with loss computation and timing, performing predictions, and comparing 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.


Dive into advanced deep learning techniques for time series analysis. Learn to structure sequential data, design and implement RNN architectures as LSTM and GRU, and build ensemble models to forecast complex temporal patterns. This course focuses on leveraging computational intelligence to capture intricate dependencies and dynamics in time series data.

Explore advanced deep learning methods for time series forecasting, focusing on LSTM architectures, sequence modeling, and ensemble techniques.

LSTM vs GRU for Time Series