Build a Large Language Model (From Scratch): Raschka, Sebastian: 9781633437166: Amazon.com: Books (original) (raw)
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The best resource to learn about LLM
The writing is very clear and thorough. The author explains a concept, then translates it into code and explain the code back to the concept again. With healthy dose of repetitions. This explanation style is really helpful to keep the flow of understanding going. I have typed the entire code from his book into VS Code and then debugged it to match the output in the book. The process is painful but the benefit is fantastic. I have bought his upcoming book "Build a Reasoning Model (From Scratch)".
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Reviewed in the United States on October 12, 2025
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.
20 people found this helpful
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Reviewed in the United States on May 5, 2026
Clear Explanations Without Hiding the Hard Parts
What I like most is that it doesn’t treat LLMs like magic. It breaks the process down step by step—from tokenization and embeddings to attention, training, and generation—so I could see how all the pieces connect.
Hands-On Learning That Actually Sticks
The examples make a huge difference. Instead of only reading theory, I was able to follow along and understand how each part works in code. That made concepts like transformers and self-attention feel much less intimidating.
Great for Going Beyond Surface-Level AI
This isn’t just a “what is AI?” book. It helped me understand the mechanics behind modern language models and gave me a much stronger foundation for experimenting on my own.
Challenging, But Worth It
Some sections take focus, but that’s part of why I liked it. It pushes you to really understand the material.
Reviewed in the United States on April 20, 2026
This is a great book. It's full of diagrams. The text is well-written.
Manning Press has a neat system, in which the print copy of the book contains a license key for the PDF of the book. I downloaded that and let my LLMs read it, too.
One person found this helpful
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Reviewed in the United States on February 20, 2026
This book is an absolute masterpiece. The writer knows how to present complex concepts in simple, absorbable ways. From concepts to labs/demoes, he makes you feel like you’re sitting in an ivy league class. The companion YouTube channel is the icing on the cake. I highly recommend this for anyone interested in learning the fundamentals of ML
4 people found this helpful
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Reviewed in the United States on December 31, 2024
What an amazing book detailing how each component of the language models components fit together and work synchronously. It is not too difficult to read / follow along if you have previous coding experience with Neural Networks and PyTorch on Machine learning projects. It definitely was a great purchase to understand what it takes to build a local LLM. I had to remove 1 star because the book already tore a bit on the front cover on day 3 of reading.
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Reviewed in the United States on September 5, 2025
This book is so-so. I wouldn't buy it again. I wanted to learn how the llm works and how the embedding algorithms are designed. Alternatively, he could have discussed the training algorithm of the llm and how the weighting matrices are determined. Alternatively, he could have discussed how the math by setting the vector spaces relate meanings to words so that an llm can convert that into something intelligible as a response. None of this was done. He presents code for llm and uses python libraries. However, it is a black box. All the discussion varies from 2 extremes of high level generalities and then specific lingo and code for particular abstractions. However, virtually nothing is made concrete. Of course some will disagree, but if I knew how llm's worked, I wouldn't write this book. If I don't understand the details of llm functions and code design with llm, this book wouldn't help much. That being said, if you want some code snippets, you will find some useful ones here.
4 people found this helpful
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Reviewed in the United States on March 30, 2026
The best book I have ever read on generative AI and machine learning. It is essential reading for anyone interested in working in the AI field. Even the appendix is highly valuable and well worth reading.
2 people found this helpful
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Top reviews from other countries
5.0 out of 5 stars Top
Reviewed in France on July 29, 2025
A great book to start and understand AI.
5.0 out of 5 stars Great book .
Reviewed in the United Kingdom on April 18, 2026
Awesome book . Not for someone who wants to go too deep but great to begin with and get good idea of basics .
5.0 out of 5 stars Me sirvió para aumentar mis conocimientos en tecnología de GenAI
Reviewed in Mexico on April 6, 2026
Buen producto, cumple lo que promete. Me sirvió para aumentar mis conocimientos en tecnología de GenAI
1.0 out of 5 stars Bad printing quality
Reviewed in the United Arab Emirates on December 5, 2025
Poor printing quality: paper is so thin so one can see letters from back side while reading front side. Also for some reason main cover is not alligned with the rest of the book.
Overall impression like it was printed at home
5.0 out of 5 stars İçerim güzel
Reviewed in Turkey on August 25, 2025
İçerik çok güzel ama ben basım kağıdını beğenmedim