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 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.

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.

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.

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!