Build a Large Language Model (From Scratch)

Great book. Easy to follow. Definitely check out the author's Videos too.

From the steps I have taken so far with this book, it is very valuable for anyone looking to start off with LLMs. Pursuing more information from the book.

I've bought tons of ML, DE, programming, cloud architecture books, etc...This book is absolutely fantastic! Especially combined by the current YouTube series published by the author (March 2025).Sebastian's Packt books are also excellent but I must say this book stands on its own. This book is extremely well written and clear, builds each component in the Transformer Architecture piece by piece, it makes me feel like I can actually build an LLM on my own.At a minimum this book will help you understand the Transformer Architecture (Attention Mechanism, Feed Forward, Layer Norm, etc...) rather than importing models from HugginFace and not really know what's going on in the background.If you are like me and are not satisfied with just building RAGs/LLM applications without understanding the model architecture, this book is for you!I'll keep buying from this author as long as the quality of his content is as good as this.

The best way to learn something is to build it for yourself, and that is exactly what this book does for LLMs. You can get explanations of how LLMs work from a lot of sites on the Internet. What this book does uniquely (as far as I know) is combine that information with a guide for you to implement it for yourself. If you finish the book and work through the code examples and exercises, you will have a solid and up-to-date understanding of how LLMs work under the hood.

This is the most in-depth book about LLMs. If you want to understand how transformers work, including every layer in their architecture, this book explains everything in detail.For example, concepts like vanishing gradients and ReLU activation are thoroughly explained, providing the mathematical intuition behind the algorithms. The appendix, spanning 80 pages, delves deeper into PyTorch and neural network concepts for additional support.The book also includes example code covering byte pair encoding, attention mechanisms, and even direct preference optimization.Im happy to have bought and read this book as this gives me better intuition how the transformer model works and how to improve llm performance with fine tuning.

I appreciated the book for its thoroughness and attention to detail. However, I believe it would benefit from being printed in color, as many images on the O'Reilly website are more vibrant and clearer when viewed in color. Additionally, enhancing the resolution of some images would improve the overall experience. For these reasons, I would rate the book 4 out of 5. With these adjustments, I think it could easily earn a perfect score of 5 out of 5.

This text is the premiere introduction for LLMs. Each chapter is carefully constructed with the detail necessary to understand how LLMs actually works behind the hood. The supplemental materials for coding prototypes, extensions, and additional deep dives for enhanced learning are readily available for any practitioner to push further. I highly recommend purchasing this material and keeping an eye of future publications from this author.

Perfect book for LLM beginners