The Hundred-Page Language Models Book

<p><strong style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>Master language models through mathematics illustrations and code?and build your own from scratch!</strong></p><p></p><p><em style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>The Hundred-Page Language Models Book</em><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)> by Andriy Burkov the follow-up to his bestselling </span><em style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>The Hundred-Page Machine Learning Book</em><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)> (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.</span></p><p></p><p><em style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>The Hundred-Page Language Models Book</em><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)> allows you to:</span></p><p></p><p><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>- Master the mathematical foundations of modern machine learning and neural networks</span></p><p><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>- Build and train three architectures of language models in Python</span></p><p><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>- Understand and code a Transformer language model from scratch in PyTorch</span></p><p><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>- Work with LLMs including instruction finetuning and prompt engineering</span></p><p></p><p><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>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.</span></p><p></p><p><strong style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>Endorsements</strong></p><p></p><p><strong style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>Vint Cerf</strong><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)> Internet pioneer and Turing Award recipient: </span><em style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>This book cleared up a lot of conceptual confusion for me about how Machine Learning actually works - it is a gem of clarity.</em></p><p></p><p><strong style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>Tomáš Mikolov</strong><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)> the author of </span><strong style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>word2vec</strong><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)> and </span><strong style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>FastText</strong><span style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>: </span><em style=background-color: rgba(255 255 255 1); color: rgba(15 17 17 1)>The book is a good start for anyone new to language modeling who aspires to improve on state of the art.</em></p>