Hands-On Large Language Models: Language Understanding and Generation
Original price was: $79.99.$19.99Current price is: $19.99.
✔️ (PDF) • Pages : 428
AI has acquired startling new language capabilities in just the past few years. Driven by rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend is enabling new features, products, and entire industries. Through this book’s visually educational nature, readers will learn practical tools and concepts they need to use these capabilities today.
You’ll understand how to use pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; and use existing libraries and pretrained models for text classification, search, and clusterings.
This book also helps you:
- Understand the architecture of Transformer language models that excel at text generation and representation
- Build advanced LLM pipelines to cluster text documents and explore the topics they cover
- Build semantic search engines that go beyond keyword search, using methods like dense retrieval and rerankers
- Explore how generative models can be used, from prompt engineering all the way to retrieval-augmented generation
- Gain a deeper understanding of how to train LLMs and optimize them for specific applications using generative model fine-tuning, contrastive fine-tuning, and in-context learning
15 reviews for Hands-On Large Language Models: Language Understanding and Generation
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Josh Longenecker (verified owner) –
The authors do an amazing job of illustrating and explaining all things LLM
Al (verified owner) –
This is one of the best books about LLM alongside Valentina Alto book on LLM’s, The author explains the LLMs in very simple terms and take one step by step to difficult concepts, one of the best books out there. Must Read..
Pen Name (verified owner) –
If you’re looking for a hands-on, no-nonsense guide to working with LLMs, this book is exactly what you need. This book stands out by taking a refreshingly practical approach, steering clear of dense mathematical theory and instead focusing on real-world applications.
One of the strongest aspects of this book is its clarity. Rather than diving deep into the underlying theoretical details of LLMs, or giving you a laundry list of libraries and frameworks, the author provides clear, step-by-step instructions on how to implement and fine-tune these models for various tasks, including text generation, sentiment analysis, summarization, chatbots, and even more advanced use cases like RAG and agents.
The code presented in Hands On Large Language Models is clear, well-structured, and easy to follow, however, given the rapid pace of development in AI, a few updates to the libraries were necessary when following along. Thankfully, these changes were minimal and easy to manage.
kein_liao (verified owner) –
i study more in statistics and computer vision. This is an amazing book that will help me grow in LLM.
Javier Hernandez Mendez (verified owner) –
Best book ever! I’m loving it, I truly recommend it
Jose Javier (verified owner) –
completo y facil de entender, sobre el funcionamiento interno de los LLM, y las librerias de hugging face y langchain , heads (transfer knowledge), chat, RAG, prompt engineering, fine tunning etc. Un libro de referencia en este area
Stillman (verified owner) –
Great book that covers all the essentials.
Meeshawn Marathe (verified owner) –
I’ve learned something new on almost every page of this book.
J. D. Taylor (verified owner) –
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.
Luis Sanabria (verified owner) –
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!
RT (verified owner) –
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.
Computational Scientist (verified owner) –
This is a great resource! The strength here is on sentence transformers, RAG, Agentic AI, and prompt engineering. This books covers those topics better than many others out there. Get this book and get started expanding your AI coding!
Christian Sellberg (verified owner) –
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.
José Alberto Santana (verified owner) –
I am blown away at how Jay Alammar and Maarten Grootendorst’s visuals blend in with the theoretical aspects of LLMs. As an 18 year old who is obsessed with the intricacies of LLMs and working with different environments like LangChain and the OpenAI API, this book felt like a playground. On another note, if you combine this with Chip Huyen’s AI Engineering textbook as well as the FastAPI framework and containerization using Docker, you’ll have the tools to deploy AI systems into production in the cloud.
Chi chi (verified owner) –
Well explained