What Topics Does The Hundred-Page Language Models Book Cover?

Updated

The book covers the path from machine learning fundamentals to modern large language models (LLMs): machine learning basics, language modeling basics, recurrent neural networks, transformers, and a large LLM chapter that includes prompt engineering and finetuning. Each topic combines mathematical explanation, illustrations, and working Python code, with PyTorch implementations and Jupyter notebooks available on GitHub. It is written for technical leaders, engineering managers, software developers, data scientists, and machine learning engineers who want both theoretical depth and practical implementation skills.

Chapter-by-Chapter Topics

The book deliberately builds understanding step by step rather than starting with transformers, which the author notes can feel overwhelming.

Chapter Topic
Foreword By Tomáš Mikolov
Preface —
Chapter 1 Machine Learning Basics
Chapter 2 Language Modeling Basics (with notebooks)
Chapter 3 Recurrent Neural Networks (with notebook)
Chapter 4 Transformers
Largest chapter Large Language Models

The progression runs from simple count-based methods through recurrent neural networks to modern architectures, so each concept rests on clear mathematical foundations established earlier.

What the Large Language Model Chapter Adds

The LLM chapter is the book's largest. It covers two practical skills:

  • Prompt engineering techniques for getting consistent outputs from models.
  • Finetuning models to follow arbitrary instructions.

These are presented as hands-on material, not just theory, so readers can adapt models to their own needs.

Practical Materials Included

  • Complete Python implementations with PyTorch on GitHub.
  • Practical Jupyter notebooks for each topic.
  • Theory, illustrations, and code combined in every chapter.
  • A free PDF copy available on request when you buy a hard copy.

Bonus: GPU Credits

Purchasing the book includes $150 in free GPU credits on Lambda. To claim them, email your proof of purchase to [email protected].

Who Should Read It

The book suits readers who need to work with text-based applications and want to understand how language models function internally. According to the site, it provides both the theoretical depth and practical implementation skills essential for working with language models, making it relevant for practitioners and engineers focused on text-based applications.