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

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.

This book sheds plenty of light into this abstract subject. By connecting the dots on the base rationale, better applications can be built. The graphics are amazing!

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.

I preordered "Hands-On Large Language Models" by Jay Alammar and Maarten Grootendorst as soon as it was available, and I've just received it. I've been eagerly anticipating this book, especially since Maarten is the author and maintainer of the BERTopic library, which has been crucial in many of my NLP projects. I'm grateful for his contributions, which have greatly supported my research efforts. This book captures that same spirit—it's truly a gem!I've dabbled with LLMs before, particularly in areas like fine-tuning models and developing autonomous agents, but this book has significantly deepened my understanding. The way they break down complex concepts with crystal-clear visuals is not just educational, but also inspiring. For instance, their explanation of transformer attention mechanisms, paired with intuitive diagrams, made an otherwise abstract topic remarkably easy to grasp. It's making me rethink how I communicate my own research—striving for a blend of depth, engaging visuals, and clear, relatable examples to make complex ideas accessible.When the authors say "hands-on," they're not kidding. Real datasets, practical coding projects, and digital resources—you're not just reading; you're doing. Jay and Maarten have managed to demystify the intricacies of large language models, particularly in chapters like the one on fine-tuning techniques, turning an intimidating topic (for those who had limited experience) into an engaging and approachable journey. Whether you're looking to cover the basics or explore the finer points, this one's a keeper.

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!

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 !

Misses important subjects

I’ve learned something new on almost every page of this book.