I often get questions from readers about how to read and get the most out of my book(s) on building LLMs from scratch. My advice is usually based on how I read technical books myself. This is not a one-size-fits-all approach, but I thought it may be useful to share.

1. Read the chapter preferably offline, away from the computer. Either classic physical form or at least on digital devices without internet. This really helps with focus time and minimizing distractions while reading. Highlighting or annotating confusing or interesting things is good, but I would not look things up at this stage. I also wouldn't run code at this stage. At least not yet.

2. On the second read-through, type up and run the code from the chapter. Copying code is tempting because retyping is a lot of work, but it usually helps me to think about the code a bit more (versus just glancing over it). If I get different results than in the book, I would check the book's GitHub repo and try the code from there. If I still get different results, I would try to see if it's due to different package versions, random seeds, CPU/CUDA, etc. If I then still can't find it out, asking the author would not be a bad idea (via book forum, public GitHub repo issues or discussions, and as a last resort, email).

3. After the second read-through and retyping the code, it's usually a good time to try the exercises to solidify my understanding. To check whether I actually understand the content and can work with it independently.

4. Go through the highlights and annotations. I would bookmark important learnings or takeaways, if relevant for a given project, in my notes documents. Often, I also look up additional references to read more about a topic of interest. Also, if I still have any questions that I feel are unanswered after my previous readthroughs and exercises, I would do an online search to find out more.

5. The previous steps were all about soaking up knowledge. Eventually, though, I somehow want to use that knowledge. So I think about which projects would benefit from what I've learned and incorporate it into them. This could involve using the main concept from the chapter, but also sometimes minor tidbits I learned along the way, e.g., even trivial things like whether it actually makes a difference in my project to explicitly call torch.mps.manual_seed(seed) instead of just torch.manual_seed(seed).

Of course, none of the above is set in stone. If the topic is overall very familiar or easy, and I am primarily reading the book to get some information in later chapters, skimming a chapter is ok (to not waste my time).

Anyway, I hope this is useful. And happy reading and learning!