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We live in an era in which contemporary artificial intelligence has become an integral part of the daily lives of millions of people, with many of these AI products being powered by large language models (LLMs). As LLM becomes increasingly important to the general public, it has become necessary for data professionals to better understand how they work in order to keep a competitive edge in the current data-driven landscape. This is why The Hundred-Page Language Models Book by Andriy Burkov is a great starting for beginners and professionals as they navigate their LLM learning journey.
This article will provide an overview of this book and what it could offer for your learning.
The Hundred‐Page Language Models Book
As the name suggests, The Hundred-Page Language Models Book aims to explore everything you need to know about language models — especially the popular and ubiquitous “large” variety. It was written by Andriy Burkov, a machine learning expert and leader with extensive experience deploying AI projects across various businesses.
The contents provide a foundational understanding of language models in a clearly explained manner, in an effort not to overwhelm the reader with the unnecessary details. The author can expertly highlight only the important topics to understand the core mechanism without straying too far into the weeds. However, the selective focus on the core might require the reader to learn through other supplemental material, such as, for example, if the reader wants to understand industrial-scale development, or any of the covered topics in greater technical detail.
Of particular note, the book is offered on a “read first, buy later” principle, where readers can explore the works first and then encouraged to support the author if they find the book valuable.
At its core, this book is a masterclass that breaks down complex language model topics into more accessible content. The book offers the reader a deep dive into the language models through several chapters, which are as follows.
Chapter 1. Machine Learning Basics
The first chapter lays the groundwork for understanding LLM by teaching us about the core concepts of AI and machine learning. Many concepts were introduced, including, but not limited to, mathematical concepts and notations such as vectors and matrices, optimization techniques, machine learning models, and more.
🎯 This chapter aims to establish the basic principles of any machine learning model to understand fundamentals that drive LMs.
Chapter 2. Language Modeling Basics
The second chapter teaches the reader more about the language model basics as we transition from general machine learning. This chapter teaches us more about how the language model handles the text data, as well as the various language model architectures. The chapter also gradually introduces basic approaches, such as bag-of-words, and then moves into more nuanced territory, such as word embeddings. The chapter also discusses language model evaluation techniques.
🎯 The second chapter introduces the foundation we need for language model and how to approach them.
Chapter 3. Recurrent Neural Network
Moving from the static text representation, chapter three introduces recurrent neural networks (RNNs) as a representation for dynamic sequence processing. This chapter specifically discusses Elman RNN, the hidden states, and the challenges to training the sequential model.
🎯 The chapter is crucial as it provides a conceptual foundation for modeling sequence data.
Chapter 4. Transformers
Once we understand RNNs, this chapter asks us to delve more in-depth into the Transformer model. The Transformer model has revolutionized NLP and become the standard of many LLMs. The chapter here focuses on the decoder-only transformer variant used in many autoregressive languages. It also covers many concepts, such as self-attention, the Q, K, and V concepts in Transformers, positional encoding, and much more.
🎯 With detailed diagrams and Python implementation, this chapter explains Transformers.
Chapter 5. Large Language Model
With the knowledge of the Transformer, we jump into the discussion about modern LLMs. In this chapter, we discuss the impact of scale, an important concept in the LLM. By increasing parameters and context size, the model can better grasp the language pattern. The chapter also discusses various training techniques and challenges in the LLM.
🎯 This covers the essence of the modern language model.
Chapter 6. Further Reading
The last chapter provides a brief overview of advanced topics related to LLM. Many concepts are touched upon, such as the mixture of experts (MoE), model merging, and model compression. This chapter acts as a conclusion and bridge to more specialized research areas in LLM.
🎯 Learn where to go next with your LM learning.
Overview
The book significantly contributes to the AI field as it could be an entry point for many beginners and professionals alike to understand LLMs. Though the title suggests brevity and conciseness with its promised short duration, the book mostly targets technical professionals. Whether you are a software developer, engineering manager, data scientist, or a curious technologist with the necessary background, this book will undoubtedly help you understand language models.
Overall, the book offers a good balance on understanding the technical foundations of language models. It is, therefore, an important book for anyone wanting to advance their knowledge in the current AI era.
Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and data tips via social media and writing media. Cornellius writes on a variety of AI and machine learning topics.