✔️ 10 Books to Understand How Large Language Models Function (2026)

1. Deep Learning
https://deeplearningbook.org
The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts.

2. Artificial Intelligence: A Modern Approach
https://aima.cs.berkeley.edu
A fundamental perspective on artificial intelligence as a comprehensive system.

3. Speech and Language Processing
https://web.stanford.edu/~jurafsky/slp3/
An in-depth examination of natural language processing, transformers, and linguistics.

4. Machine Learning: A Probabilistic Perspective
https://probml.github.io/pml-book/
An exploration of probabilities, statistics, and the theoretical foundations of machine learning.

5. Understanding Deep Learning
https://udlbook.github.io/udlbook/
A contemporary explanation of deep learning principles with strong intuitive insights.

6. Designing Machine Learning Systems
https://oreilly.com/library/view/designing-machine-learning/9781098107956/
Strategies for deploying models into production environments.

7. Generative Deep Learning
https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf
Practical applications of generative models and transformer architectures.

8. Natural Language Processing with Transformers
https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html
Methodologies for constructing natural language processing systems based on transformers.

9. Machine Learning Engineering
https://mlebook.com
Principles of machine learning engineering and operational deployment.

10. The Hundred-Page Machine Learning Book
https://themlbook.com
A highly concentrated foundational overview without extraneous detail. 📚🤖