The Transformer chapter is online!

Hey! I’ve been thinking about a special Christmas gift for my subscribers. How about the sixth chapter of my upcoming The Hundred-Page Language Models Book which I just put online (in addition to the other five chapters)?

In this chapter, you’ll read about the Transformer architecture, exploring:

• The decoder block
• Self-attention
• Multi-head attention
• Rotary position embeddings (RoPE)
• Residual connections
• Root mean square normalization (RMSNorm)

You’ll find plenty of math, illustrations, and Python code. By the end, you’ll have trained your own Transformer-based language model from scratch.

What better way to spend the holidays than by learning something new from a fun to read book?

Enjoy and Happy Holidays!