Summary
This chapter demonstrates how to implement and swap between two recurrent neural network cell types—LSTM and GRU—by adjusting their gate configurations and model instantiation in code, then comparing their convergence behavior, performance metrics, and training time.

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.


Master Recurrent neural networks and their advanced variants like LSTMs and GRUs using PyTorch. Gain hands-on experience processing sequential data for practical applications. Apply these powerful models to tackle real-world challenges in time series forecasting and various Natural language processing tasks.

Covers the limitations of traditional neural networks for sequential data and introduces the fundamentals of Recurrent Neural Networks. Explains RNN architecture, types, and step-by-step implementation through basic examples and a coding challenge.

Explores common training challenges such as vanishing and exploding gradients. Introduces advanced RNN variants including LSTM and GRU, highlighting their internal mechanisms and use cases, with practical implementation examples for each.

Focuses on processing and forecasting time series data using RNN-based models. Includes data loading, preprocessing techniques, model training, and performance evaluation, with emphasis on comparing LSTM and GRU architectures.

Demonstrates the application of RNNs to text classification tasks. Covers core NLP concepts, text encoding methods, data preparation steps, and construction of an LSTM-based model for sentiment prediction.

LSTM vs GRU for Time Series