Introducing The Big Book of Large Language Models!

For the past years, I have been creating educational content around machine learning and, specifically, large language models. I have been acquiring a depth of knowledge through my experience and practice in the field, and I want to share it with everybody! I started the process of writing, I believe, one of the most complete books on the subject of Large Language Models. You can access the book website here: The Big Book Of Large Language Models.

I will make the chapters available little by little as I write them. Don’t hesitate to leave comments so I can improve the current draft! The first chapter is now available: Language Models Before Transformers. In that chapter, I address the following subjects:

• The Embedding Layers

• Word2Vec

• GloVe

• The Jordan Network

• The Elman Network

• The Vanishing and Exploding Gradients Problem

• Long Short Term Memory (LSTM)

• Gated Recurrent Unit (GRU)

• Sequence-to-Sequence Models

• The RNN Encoder-Decoder Architecture

• The Bahdanau Attention Mechanism

• The Luong Attention

Here are the chapters coming up:

1. Introduction

2. Language Models Before Transformers

3. Attention Is All You Need: The Original Transformer Architecture

4. A More Modern Approach To The Transformer Architecture

5. Multi-modal Large Language Models

6. Transformers Beyond Language Models

7. Non-Transformer Language Models

8. How LLMs Generate Text

9. From Words To Tokens

10. Training LLMs to Follow Instructions

11. Scaling Model Training

12. Fine-Tuning LLMs

13. Deploying LLMs

My philosophy is to provide the depth of the mathematic notation along with the ease of visual illustrations of the different concepts. I believe the book can be read at different levels:

• For somebody looking for the finest details, the equations should provide the foundations to understand thoroughly the concepts.

• For somebody looking for a simpler read, the equation can be ignored to focus on the textual and visual explanations.

• For somebody looking to strengthen their mathematical fundamentals in ML, the connection between the math and the visuals should help bridge the difficulties usually encountered when learning mathematics.

Let me know if you think the book is missing the target on that “mission.” I am truly excited to share this with you! I hope you will enjoy reading it as much as I enjoy writing it!