LLM Chronicles #4.2: RNN and LSTM Cells in PyTorch (Lab)

In this lab session of the LLM Chronicles, we dive deep into the world of Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) cells in PyTorch. These advanced neural network architectures are used extensively in natural language processing tasks, time series forecasting, and many other sequential data applications.

RNN Cells

RNN cells are a type of neural network cell that has feedback connections. This allows them to capture temporal dependencies in sequential data. In this lab, we will learn how to create and train RNN cells using PyTorch.

LSTM Cells

LSTM cells are a type of RNN cell that has additional gating mechanisms to control the flow of information. This allows LSTM cells to better capture long-term dependencies in sequential data. In this lab, we will explore the architecture of LSTM cells and learn how to use them in PyTorch.

Lab Exercises

During the lab session, we will work on several exercises to get hands-on experience with RNN and LSTM cells in PyTorch. We will build and train models on sample datasets to understand the capabilities of these powerful neural network architectures.

Conclusion

By the end of this lab session, you will have a solid understanding of RNN and LSTM cells in PyTorch and how to use them in your own neural network projects. These powerful tools will open up new possibilities for solving complex sequential data problems.