I did the same. I got "Build Your Own LLM From Scratch" last christmas and it is a great resource for learning how LLMs work, from tokenization to generating text. The author makes a great job explaining each new concept. If you know PyTorch (or at least know about tensors), the material will be approachable for you.

Sebastian, if you are reading this, this is an idea for another book: how to orchestrate agents for adversarial code review. I've been looking for learning resources to make something similar to this [1] at a smaller scale. I am aware of codex subagents, though. However, given that Sebastian already has a book for LLMs and another book on planning, I think adversarial code review is an interesting next step.

[1] https://bun.com/blog/bun-in-rust

[2] https://learn.chatgpt.com/docs/agent-configuration/subagents...

I don't like working with neural networks and I always found everything overcomplicated or oversimplified, sometimes both at the same time.

But this book is almost perfect. Every new concept is well described and every is well founded. And every step moves you forward (if you know, you know)

I read it the first time with a study group which was very helpful.

I like it too. Haven't got very far through it yet but just learning about how to use PyTorch and finally getting how back propagation works via autograd etc. was really fun and enlightening.