Hands-on Large Language Models: Language Understanding and Generation

Great book with nice color print, page quality and packaging, lightning fast delivery.

completed reading Hands-on Large Language Models by Jay Alammar and Maarten Grootendorst, and I must say, it’s a gem. The book offers a StatQuest-like experience for LLMs, starting from models like BERT to the preference tuning of large language models (LLMs). The explanations are intuitive, and the visuals make the learning experience engaging. Finally understood BertTopic and I can’t help but think about how implementing augmented SBERT for certain tasks at my previous company could have made a significant impact. Although it comes with a premium price tag, I believe it’s worth every penny for the visuals and clarity it offers. I wish it had included more on scaling laws, given their significance in understanding the exponential improvements and cost trade-offs in training LLMs. Including a discussion on that would have rounded off an already exceptional book.

Book was in good condition.

Nice

Excellent and very well written !!

I read 3 chapters so far and are awesome 👍

This book contains beautiful explaination of working with transformers and llms

I'm halfway through this gem and had to share my excitement. If you're looking to truly understand LLMs, this is your resource. What makes it such an exceptional read: • Both authors are renowned for their visual explanations of complex ML concepts, and this book is a masterclass in making the complicated digestible • It builds intuition rather than just explaining mechanics—exactly what practitioners need • The practical applications section bridges theory to implementation brilliantly, along with lots of code blocks that you can reuse. The book doesn't just explain how LLMs work—it changes how you approach using them. The mental models I've gained have already improved my approach to using LLMs for building products.

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.

nice book

This book is great but it depends a bit on the purpose you have. I have much background in general data science methods like ML and python programming but I lack knowledge on how LLMs work and how to use them which I wanted to fill in by reading. This book provided me exactly that amazingly well: Based on prior knowledge on python programming and basic data science (regression, clustering, dimension reduction etc) it provides an extremely good high level understanding and intuition on how LLMs work and it's usecases and the required code for it. I can now competently use existing models to approach text based data science tasks like classification, sentiment analysis and so on and even fine tune them to some degree. It does however not teach you how how to code LLMs from scratch, how the math works in detail and so on Anyways, if you want to go there I would still recommend starting with this book and then dive deeper with other books, as the high level discussion and applications this book provides are incredibly clear and well structured so you will have all the intuition in place which will benefit when going into the deep details. When you are one of those data folks like me however that just want to understand what LLMs can do and how to use them this is all you need.

Väldigt pedagogisk och utbildande med många illustrationer som är både vackra och beskrivande som ger läsaren en bättre förståelse över innehållet i boken.

Not yet read through but can already say that's a great book.