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The book is amazing. Much better than I expected. I was minimally familiar with neural networking techniques (finished 6-months course on Coursera, and by now have forgotten most of it). So, I had a vague idea about forward and backward propagation, remembered such terms as dropout, normalization etc. without actually remembering their meaning. From the Andrew Ng course I remembered the term "transformer" (since he had a few good introductory explanations of it), but by now I completely forgot how it works. My knowledge of Python was very limited (and mostly forgotten). I knew nothing about PyTorch. When I saw the references to the book on Facebook, I decided that it might be helpful for me to recall these concepts, and especially interesting was to learn the concept of transformers and self-attention which I knew belong to the foundation of modern LLMs.
The book exceeded my expectations. It is written in an excellent methodical style. Introduces concepts one by one, helps experimenting with them in the real code. It provided an excellent introduction to PyTorch (in Appendix A, which the author recommended to consume before reading the rest of the book). The introduction is short, not overwhelming the reader with millions potential concepts of the huge ecosystem of Python and PyTorch, and still sufficient for productive consuming the entire book that uses both. All the concepts are defined in easy-to-consume steps, leading eventually to a complete overall understanding of GPT model. I am not naive to think that I can develop LLMs by myself now, but I definitely got more than expected. And enjoyed the material a lot.
I did not use the code from GitHub (by the book's reference). Instead, I meticulously re-entered all the examples from the book's text into several Jupyter Notebooks in VSCode. This way I moved a bit slower but understood material better. Even found a few minor (typo-level) issues in the code.
I am working on an ordinary Surface Book (no GPU), and all examples work instantaneously so far (obviously, it will change when I come to training). I am now in the position after chapter 4: Built the untrained GPT model and cannot wait when I will start training and using it.
Highly recommend the book to everyone who wants to make their hands "dirty" with the AI.
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From the Publisher
“The most understandable and comprehensive explanation of language models yet! Its unique and practical teaching style achieves a level of understanding you can’t get any other way.”
Cameron Wolfe, Senior Scientist, Netflix
“Sebastian combines deep knowledge with practical engineering skills and a knack for making complex ideas simple. This is the guide you need!”
Chip Huyen, author of Designing Machine Learning Systems and AI Engineering
“Definitive, up-to-date coverage. Highly recommended!”
Dr. Vahid Mirjalili, Senior Data Scientist, FM Global
why this book?
Build a Large Language Model (From Scratch) offers a practical, hands-on approach to understanding and constructing large language models (LLMs) from the ground up.
By guiding you through each stage—from data preparation and coding attention mechanisms to pretraining and fine-tuning—this book demystifies the inner workings of LLMs using Python and PyTorch.
Ideal for developers and machine learning enthusiasts, it empowers you to build a functional GPT-style model on a standard laptop, fostering a deeper comprehension of generative AI technologies.
about Manning
Manning helps developers and tech professionals stay ahead in a fast-moving industry with expert-led books, videos, and projects. Learning never stops, but it’s hard to keep up, so we focus on content that’s practical, clear, and trusted. As an independent publisher, we adapt quickly, from pioneering early-access books to offering DRM-free eBooks. Our series, like "In Action" and "In a Month of Lunches", reflect a commitment to making complex topics accessible.
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| Price | EUR 43.32 | EUR 35.60 | EUR 51.29 | EUR 46.29 | EUR 31.20 | EUR 44.34 |
| Level of proficiency | Intermediate | Intermediate | Intermediate | Intermediate | Intermediate | Advanced |
| About the reader | For data scientists and ML engineers. | For intermediate Python programmers. | For intermediate Python programmers. | For developers, engineers, and product managers. | For data scientists and data analysts. | For data scientists and machine learning engineers. |
| Special features | Includes liveBook with out built-in AI assistant. | Includes liveBook with out built-in AI assistant. | Includes liveBook with out built-in AI assistant. | Includes liveBook with out built-in AI assistant. | Includes liveBook with out built-in AI assistant. | Includes liveBook with out built-in AI assistant. |
| Pages | 456 | 344 | 688 | 328 | 232 | 520 |
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About the author
Great book to learn inner working of LLM
Explains the Transformer’s self-attention mechanism step-by-step — from text → token embedding → positional embedding → Query * Key → attention scores → attention weights → attention weights * Value → Context vector — using a simple 6-word example in PyTorch. The book concludes with a chapter on instruction fine-tuning implementation on GPT-2 open model. A great read if one is interested to build intuition about self-attention in Transformers and inner system workings of LLM model. A must keep in your book shelf!!
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VizekanzlerReviewed in Germany on December 11, 2025
Einfach die Bibel für LLM-Einsteiger
Format: PaperbackVerified Purchase - vushesh babuReviewed in the United Kingdom on April 18, 2026
Great book .
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FamMurilloJimenezReviewed in Mexico on April 6, 2026
Me sirvió para aumentar mis conocimientos en tecnología de GenAI
Format: KindleVerified Purchase - KirillReviewed in the United Arab Emirates on December 5, 2025
Bad printing quality
Format: PaperbackVerified Purchase
