Hands-On Large Language Models: Language Understanding and Generation 1st Edition

The book is pretty comprehensive. Each chapter really packs a punch. After trying to piece different concepts together, chapter 3, really made transformers click for me. I also really enjoyed the organization of the earlier chapters that talked about the various techniques as solutions to earlier problems. It gives the reader a sense of the intent and purpose of each component or technique. This isn't a "dive into" type of book even thought it does have some good code samples. The amount of information per page is dense so it make take some time to fully grok each page but it is well worth the effort.This is really a book for people who want to deep dive and aren't there just to copy and paste code until it does something.Funnily enough, a great study companion for this book is ChatGPT or any other similar LLMs. There are parts that may be confusing and ChatGPT and Claude are both great at explaining the book/themselves.

As a resident of Sweden, I was thrilled to discover the Kindle version of this book, allowing me to dive in immediately without waiting for international shipping. From the moment I started reading last week, I've been completely engrossed. The authors' approach is brilliantly practical, seamlessly blending theoretical explanations of Language AI and LLMs with hands-on .ipynb exercises that bring concepts to life.The visuals are simply outstanding, offering incredibly detailed insights into the inner workings of LLMs. I particularly appreciate the balanced coverage of both open-source and licensed models, providing a comprehensive view of the field.I've been so impressed that I've already started sharing the book with a friend, who finds it equally enlightening. The clarity and depth of the content make it an invaluable resource for anyone interested in LLMs.I'm confident that this book will inspire countless innovations and breakthroughs in the field. Jay and Marteen have created a truly phenomenal work that's both educational and inspiring. Thank you for this exceptional contribution to the AI community!

As someone who works in machine learning but mostly on CV problems, this book was a perfect bridge into the world of language models. It doesn’t assume you’re a total beginner, but it also doesn’t dump you in the deep end with dense theory and academic papers. The authors do a great job of grounding concepts in clear explanations and walk-throughs you can actually run.What stood out for me:• ✅ Hands-on notebooks + code to reinforce each concept• ✅ Explains transformer internals without getting lost in math• ✅ Covers modern workflows — from fine-tuning to inference• ✅ Clean visualizations (if you know Jay Alammar’s style, you know)Also, Maarten’s sections on vector databases, embeddings, and RAG workflows were super relevant for production applications. You can tell both authors have experience teaching and shipping real-world stuff.⚠️ Minor caveat: This isn’t a deep theoretical text — if you’re looking for the type of math found in something like “Deep Learning” by Goodfellow, this isn’t it. It’s much more about doing.If you’re a data scientist, ML engineer, or just a curious dev looking to go beyond ChatGPT and understand how to work with LLMs at a system level — grab this book. You’ll get a lot out of it.

Focused, concise, and to the point. Well-structured with thoughtfully chosen topics. I hope the book continues to be updated and expanded to cover more ground as the field evolves.

This is an enjoyable and accessible read with many of the concepts behind LLMs covered. The code examples are fun and they've picked models that anyone can run on Colab (be warned - if you have an intel-era mac they won't often won't run locally since PyTorch dropped support for non-Apple silicon). At the time of this review (mar 25) the book is pretty up to date too.Areas for improvement? I'd like to see a bit more attention (ha ha) paid to training.

The book is well written, but it relies on intuitive descriptions and (mostly unexplained) diagrams to present the various models. I found that confusing, since an intuitive explanation is not as precise as one using mathematics, thus my three stars. Looking for other books on this topic, I discovered that most of them are similar. So, obviously there is a market for such kind of books. (There is also a huge number of posts in the Internet, but most of them are poorly written, and they leave you in the same state of ignorance as before !!) So, I guess it's up to what you are looking for. If you want a book where these models are presented in a rigorous and unambiguous manner, then I recommend "Mathematical Engineering of Deep Learning by Benoit Liquet, Sarat Moka, Yoni Nazarathy. There maybe other similar books, but that's the only I found so far. My 2-cent's worth !

I really recommend this book for people who would like to understand the concept of LLM in a simple way. There is a free course on Coursera reflecting some parts of the book. Really enjoyed reading it.

Misses important subjects