It helps if you have some basic linear algebra, for sure - matrices, vectors, etc. That's probably the most important thing. You don't need to know pytorch, which is introduced in the book as needed and in an appendix. If you want to really understand the chapters on pre-training and fine-tuning you'll need to know a bit of machine learning (like a basic grasp of loss functions and gradient descent and backpropagation - it's sort of explained in the book but I don't think I'd have understood it much without having trained basic neural networks before), but that is not required so much for the earlier chapters on the architecture, e.g. how the attention mechanism works with Q, K, V as discussed in this article.

The best part about it is seeing the code built up for the GPT-2 architecture in basic pytorch, and then loading in the real GPT-2 weights and they actually work! So it's great for learning but also quite realistic. It's LLM architecture from a few years ago (to keep it approachable), but Sebastian has some great more advanced material on modern LLM architectures (which aren't that different) on his website and in the github repo: e.g. he has a whole article on implementing the Qwen3 architecture from scratch.