Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs (Addison-Wesley Data & Analytics Series)

"Quick Start Guide to Large Language Models" by Sinan is a transformative read for anyone interested in the rapidly evolving field of AI and LLM. This book serves as an exceptional guide, making complex concepts of Large Language Models (LLM) accessible to readers from all backgrounds, offering clarity and in-depth understanding.Additionally, Sinan takes a practical approach to his book. He provides comprehensive insights and examples into how to interact and work with these models effectively. This aspect was incredibly helpful, and I gained a much deeper and more confident understanding of LLMs and their applications.Rating: 10/10. A highly recommended read that offers valuable knowledge and confidence in the world of Large Language Models and AI.

The author knows the material and clearly does an excellent job of communicating it, in a way for novices to understand. He steadily builds up the reader's confidence with LLMs. I enthusiastically recommend this book for anyone new to the area.Update: If you're looking to purchase this content in 2024, actually, I instead recommend the second edition. While either will do a great job of teaching you, the new edition includes updated models and new chapters.

The book is quite comprehensive but the code base needs to be updated. I don’t see complete relevant codes on the repo. Will update to 5* once repo is good. Thanks

Good theoretical overview but appreciated the practical examples for actual uses

There's a lot of content out there about prompting and gimicky tricks out there on the web and courses etc. This book avoids the nonsense and goes straight to the point, for programmers to be able to really use LLMs for their own applications. You don't have to be a ML PhD (in fact, that's a different beast) for this book. Do you want to use LLMs to pipeline your novel application? If the answer is yes, buy this book now.

It has been long since I found a truly excellent tech book and for such a groundbreaking field like LLMs. The author is very experienced and does share his insight and tech knowledge with us. It felt like having a mentor next to you. It is best if you have some background in NLP and ML. Every line in the book counts. The books just need a better-quality printing and some layout/graphics improvements - maybe ChatGPT can help there?

I have to admit that I am not a fast reader, and I usually don't read technical books; I just keep them for reference, but I devoured Sinan Ozdemir's book on LLMs. It has the right mix of theory, examples, and code to keep me interested.Now I've got more tools to start looking at, for sure.

I gave this book a single star because I couldn't read it due to printing issues. I may also return it. I tried hard to read the first chapter. However, my eyes were really tired, and I had a headache. I don't think this book is cheap to be printed in this bad way. I could print it on my Printer in better quality and for less price!The Amazon sample is in colors; however, the book is in black and white, and understanding many images depends on different colors.This is not the issue that held me from reading the book. Here are the real printing issues:- The font Size on the left page differs from the one on the right. Even the introduction in font 8 or 9 and the reset in 10 or 11.- The words on the left page are hazy, and the words on the right page are normal sharp. This gives the impression that your eyes have some issue or are crossed together!!-The left page tends to be yellow, and the right one tends to be blue, creating a confusing reading experience.- Some parts of the pages are dark, and others are light, which makes you uncomfortable.- The image quality is very bad; almost all the images are pixelated.- Because all images are in gray with bad quality, their details are totally obscured.

This book is a well-structured guide that adeptly bridges the gap between theoretical concepts and their practical application in the field of LLMs, an aspect crucial for professionals like myself.The initial chapters of the book provide a solid foundation, introducing key concepts of LLMs in a manner that is both thorough and accessible. This sets the stage for deeper exploration into more complex topics.Key Highlights:Foundation Building: The first chapters offer a comprehensive introduction to LLMs, essential for understanding their fundamental workings and capabilities.Practical Application: The book translates theoretical knowledge into practical scenarios.Advanced Topics Coverage: In-depth exploration of modifying model architectures, embeddings, and next-generation models, providing insights for advanced solution design.Hands-On Examples and Case Studies: Practical examples and real-world case studies enable architects to visualize the application of concepts.Trends and Future Outlook: Discussion on multimodal Transformer architectures and reinforcement learning keeps readers abreast of the latest trends in LLMs.What stands out in Ozdemir’s book is its comprehensive coverage of topics relevant to LLMs. It dives into essential areas such as semantic search, effective prompt engineering, and the fine-tuning of these models.The practical guidance provided in the book is its most significant strength. The hands-on examples and case studies are particularly beneficial as they translate theoretical knowledge into actionable insights.Furthermore, the book’s exploration into more advanced topics, such as modifying model architectures and embeddings and insights into next-generation models, is highly beneficial.The book is well-organised in content delivery and structure, making it easy to follow and reference. The clarity of explanations helps in demystifying complex topics, making them digestible for professionals who may need a deeper background in machine learning or NLP but are keen to apply these technologies in their projects.

Starts well introducing general concepts but it fails to deepen them with actual notions.Others have mentioned that code samples are incomplete.The writing is sometimes unclear, perhaps it needed more proof-reading.All in all, I couldn't keep reading it to the end; it took me more to understand what the author wants to say than the technical parts.

La exposición es muy intuitiva y con graficos que hacen comprender mejor los problemas de la tecnología LLM.