The Hundred-Page Language Models Book: hands-on with PyTorch (The Hundred-Page Books)

Andriy Burkov’s The Hundred-Page Language Models Book exemplifies the principle that brevity is the essence of wit. In just over 100 pages, Burkov provides a resource with the depth and quality of a college-level textbook. Truthfully, I wish I had texts this good when I was studying engineering. Its compact format delivers a comprehensive treatment of its subject matter and nothing more. I appreciate an author that does not sell past the close.The book employs concise descriptions, precise mathematics, relevant code examples, and well-designed diagrams to convey complex topics effectively. These elements are integrated with care, ensuring that key concepts, such as language model architectures, are accessible without sacrificing technical accuracy. I had not had any experience with PyTorch prior to working through the examples in the book, so I spent a little more time understanding PyTorch and it was time well spent.The structure is methodically progressive, with each chapter building on the previous one. This approach ensures that by the time the transformer architecture is introduced, readers have the necessary context to understand its significance and mechanics. I would challenge the reader to find a better or more clear explanation of the transformer. The logical flow enhances the book’s utility and helps the reader understand the evolution of NLP and why transformers are so effective. Burkov is careful to set expectations and takes the time to clearly identify the limitations of LMs.Additionally, the accompanying wiki serves as a valuable supplement, providing further detail and updates to the material. Together, the book and wiki constitute a robust resource for studying language models. After the book was published he added some additional material on RL and GRPO in light of the DeepSeek release, which was an effective use of the wiki. This text is highly recommended for those seeking a clear, concise, and well-constructed introduction to the field.

As a Senior Data Scientist with experience in NLP and Large Language Models, I found The Hundred-Page Language Models Book to be a breath of fresh air. Andriy Burkov presents a structured journey, stepping through machine learning and neural network fundamentals before diving into word vectors & embeddings, transformers, fine-tuning, and real-world applications.What makes this book stand out is its clarity, engaging style, and the way visuals are used to explain concepts. The thoughtful use of colors in illustrations enhances make some complex ideas more intuitive. The accompanying Jupyter notebooks provide hands-on PyTorch examples, and the inclusion of $150 in free Lambda GPU credits is a great touch for those looking to experiment with training models.Whether you're planning to build LLM solutions or looking for a concise yet engaging refresher, this book is a fantastic resource. Highly recommended, and a great addition to the successful 'The Hundred Page's series.

Do you know the difference between a bag of words and word embeddings?Do you know the difference between a pre-trained or base model and a fine-tuned model?What if you are asked to evaluate an LLM model? Elo ratings are not just for chess.Getting to the essence of a complicated subject is a hard skill.There are few technical writers who can do this, and I’ve read hundreds of technical books in my lifetime.Andryi is one of those one-of-a-kind writers who has demonstrated again and again that he can deliver.Transformers, word embeddings, self-attention, skip-connections—by the end of the book, you will not only master how all of these concepts work together, you’ll be able to code and train a large language model for fun and profit.But don’t take my word for it.Buy the book, learn the cool stuff, and then share it with the world!

A masterpiece of concision that delivers exactly what it promises. Unlike other ML books that dive straight into transformers, this guide builds understanding step by step - from count-based methods to modern LLMs. The balance of math, illustrations, and working Python code makes complex concepts approachable. Each chapter comes with practical Jupyter notebooks and complete PyTorch implementations on GitHub. Whether you're a developer or ML engineer, this book provides both theory and hands-on skills in just 100 pages. A worthy addition to Andriy's 'Hundred-Page' series.

Andriy's book excels at explaining complex LLM and machine learning concepts with practical code examples. It's concise, clear, and avoids the common pitfalls of overly technical books. The active GitHub updates, including recent additions on GRPO and reinforcement learning, make it an invaluable resource!

Excellent introduction in a condesed and readable format. Very fine quality of book, as in, printing, font, etc, as well so pleasant reading experience. I moved from an adjacent area to working with these things and this book was one of the steps. Also, its fun!

I was incredibly impressed by Burkov's approach when I read his machine learning book, and now he's done it again with large language models. His "read first, buy later" strategy shows such respect for his readers, and the content is just brilliant—deep insights paired with crystal-clear visuals that make complex ideas click. As an economist, I surprised myself by wanting physical copies of both books, but some things are worth more than their price tag. These aren't just technical guides—they're the kind of books I want my kids to discover on our shelves years from now, when they're trying to understand how this technological revolution unfolded.