Build a Large Language Model (From Scratch)

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.

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

I was able to instantly take what I learned from this book and find out why past AI projects failed and how to apply it the the project I'm currently working. Even with up to date college classes, the subject area is so vast.

I loved the details and step-by-step approach used in the book. If it is possible, I suggest to also purchasing the videos in Manning which help to understand the concepts. I have paid much more for some online curses from MIT about DNN, and this book alone have a lot of content also in DNN, although it is not their focus

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.

I think this book is an excellent introduction to the practical aspects of building a Large Language Model (LLM). It illustrates the use of the fundamental components of building LLMs by having the reader follow along by constructing a “toy LLM”; basic linear algebra (e.g. matrix multiplication, matrix transposes), gradient decent optimization, random number generation, and ad hoc aspects of training a model effectively, and the large quantities of digital data (e.g. text, video, images, sound) needed to build a model. My key takeaway from reading this book is that scaling this approach to ever larger datasets combined with ad hoc architecture and parameter tweaks have nothing in common with human intelligence which is far beyond the simplistic toolset used in constructing LLMs. To think otherwise is naive at best and foolish at worst. Relying on this stochastic-by-nature approach to important human endeavors is setting oneself up for some unexpected catastrophic failures.

This book is the most comprehensive yet accessible text I’ve read covering the foundation of Generative AI. There is no better way to master this field than to develop a model from scratch. Sebastian takes you through each step, not skipping detail, until you are confident in coding a model yourself. I highly recommend this book.

The best LLM book that I have read so far! Crystal clear.

A great book to start and understand AI.

Für LLM-Einsteiger gibt es kein besseres Buch!

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

İçerik çok güzel ama ben basım kağıdını beğenmedim

Zeer goed om zelf je eerste LLM te leren maken.