Readings Newsletter
Become a Readings Member to make your shopping experience even easier.
Sign in or sign up for free!
You’re not far away from qualifying for FREE standard shipping within Australia
You’ve qualified for FREE standard shipping within Australia
The cart is loading…
This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Master language models through mathematics, illustrations, and code-and build your own from scratch!
The Hundred-Page Language Models Book by Andriy Burkov, the follow-up to his bestselling The Hundred-Page Machine Learning Book (now in 12 languages), offers a concise yet thorough journey from language modeling fundamentals to the cutting edge of modern Large Language Models (LLMs). Within Andriy's famous "hundred-page" format, readers will master both theoretical concepts and practical implementations, making it an invaluable resource for developers, data scientists, and machine learning engineers.
The Hundred-Page Language Models Book allows you to:
Master the mathematical foundations of modern machine learning and neural networks
Build and train three architectures of language models in Python
Understand and code a Transformer language model from scratch in PyTorch
Work with LLMs, including instruction finetuning and prompt engineering
Written in a hands-on style with working Python code examples, this book progressively builds your understanding from basic machine learning concepts to advanced language model architectures. All code examples run on Google Colab, making it accessible to anyone with a modern laptop.
Endorsements
Vint Cerf, Internet pioneer and Turing Award recipient: "This book cleared up a lot of conceptual confusion for me about how Machine Learning actually works - it is a gem of clarity."
Tomas Mikolov, the author of word2vec and FastText: "The book is a good start for anyone new to language modeling who aspires to improve on state of the art."
$9.00 standard shipping within Australia
FREE standard shipping within Australia for orders over $100.00
Express & International shipping calculated at checkout
This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
Master language models through mathematics, illustrations, and code-and build your own from scratch!
The Hundred-Page Language Models Book by Andriy Burkov, the follow-up to his bestselling The Hundred-Page Machine Learning Book (now in 12 languages), offers a concise yet thorough journey from language modeling fundamentals to the cutting edge of modern Large Language Models (LLMs). Within Andriy's famous "hundred-page" format, readers will master both theoretical concepts and practical implementations, making it an invaluable resource for developers, data scientists, and machine learning engineers.
The Hundred-Page Language Models Book allows you to:
Master the mathematical foundations of modern machine learning and neural networks
Build and train three architectures of language models in Python
Understand and code a Transformer language model from scratch in PyTorch
Work with LLMs, including instruction finetuning and prompt engineering
Written in a hands-on style with working Python code examples, this book progressively builds your understanding from basic machine learning concepts to advanced language model architectures. All code examples run on Google Colab, making it accessible to anyone with a modern laptop.
Endorsements
Vint Cerf, Internet pioneer and Turing Award recipient: "This book cleared up a lot of conceptual confusion for me about how Machine Learning actually works - it is a gem of clarity."
Tomas Mikolov, the author of word2vec and FastText: "The book is a good start for anyone new to language modeling who aspires to improve on state of the art."