Hands-On Large Language Models: Language Understanding and Generation

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. Good structure. Well chosen subjects. Hope the book will be continue 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.

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 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!

I'm still reading, so my opinion may change, but I'm not liking it so far.One of the things I hate in books is use of undefined terms. This book does it constantly. It's a structural decision - they give overviews, then eventually get around to explaining the pieces I the overview.But that's terrible, as the diagrams make no sense, until you understand the components. So you have to read the book twice to make any sense of it.I went looking for an example. Figure 3-6, p78. There are three things in the input path: tokenizer, transformer block, and LM head. Of the three, only the tokenizer has been explained. Transformer block are later explained on p87. LM head does not seem to be explained at all. Unless you think "simple neural network layer" is an explanation.Most books are much better about this, and make an effort to avoid doing what this book does constantly.From the other reviews, some people seem to like this format. I hate it.Not recommended.

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!

Misses important subjects