Chapter 5: Recurrent Neural Network

Chapter 5: Recurrent Neural Network of my upcoming The Hundred-Page Language Models Book is now online

I just put online the fifth chapter of my upcoming book on language models! This chapter explores Recurrent Neural Networks (RNN) -- the architecture that revolutionized how machines handle sequences. It covers everything from the mathematical foundations of parametric language models to a complete PyTorch implementation of an RNN-based language model.

The chapter explains key concepts like:
- How RNNs process sequences and maintain memory through hidden states
- The embedding layer and why it's crucial for language models
- Training with mini-batches using PyTorch's Dataset and DataLoader
- Loss computation and the mechanics of learning to predict the next token

After reading this chapter, you will have coded and trained your first neural language model from scratch!

Check out the full new chapter as well as the preceding chapters on the books website and let me know what you think!