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Code and materials for my book "50 ML projects to understand LLMs"
| Date | Stars |
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| 2026-07-31 | 484 |
| 2026-08-06 | 489 |
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# 50 ML projects to understand LLMs Code repository for the book "50 ML projects to understand LLMs: Investigate transformer mechanisms through data analysis, visualization, and experimentation" by Mike X Cohen, PhD. ## About This Repository This repository contains all Python code and Jupyter notebooks for the 50 projects in the book. Each project includes: - **Helper notebook**: Incomplete code to work through the projects yourself (hints and guidance are in the book). - **Solutions notebook**: Complete, working implementation corresponding to the detailed explanations in the book. All code runs on Google Colab, so you don't need to install anything locally or manage library configurations. <img width="555" src="https://m.media-amazon.com/images/I/81tmONmAReL._SY522_.jpg" /> ## About the Book Learn how LLMs like GPT and BERT actually work by applying machine learning techniques to their internal activations. This book takes a unique approach: rather than building LLMs from scratch or using them via APIs, you'll investigate their mechanisms by treating hidden states, attention patterns, and embeddings as data to analyze. Through 50 hands-on projects, you'll learn to: - Inspect and visualize transformer internals - Analyze attention mechanisms and layer dynamics - Apply statistical and causal methods to understand model behavior - Manipulate activations to test hypotheses about LLM mechanisms Each project teaches skills in three areas: machine learning techniques, LLM mechanisms, and Python coding with data visualization. ## Overview of projects, ML skills, and LLM concepts [Check out the spreadsheet for detailed info](https://docs.google.com/spreadsheets/d/1kPiFzCYat-gfjuZthXpLyJ1TZtIemYl-BL18rg37KhA/edit?usp=sharing) [Check the pdf](https://github.com/mikexcohen/ML4LLM_book/blob/main/ml4llm_TOC_ch1.pdf) for table of contents and chapter 1 (introductions). ## Purchase the Book | Format | Link | |--------|------| | Paperback | [amazon](https://www.amazon.com/Projects-Understand-LLMs-visualization-experimentation-ebook/dp/B0H1D5ZFVH) | | PDF | [Gumtree](https://mikexcohen.gumroad.com/l/ml4llms) | No installation required — all notebooks run directly in Google Colab. ## Discord server Join the Discord server for questions and support: https://discord.gg/t9UAkKyR95 ## Prerequisites - Python programming experience (beginner to intermediate level) - Basic familiarity with machine learning concepts (helpful but not required) - Curiosity about how LLMs work The book introduces ML techniques as needed, so you don't need to be an ML expert to get started. ## Citation If you use this code in your research or projects, please cite this GitHub url ## License This code is released under the MIT License. See LICENSE file for details. ## About the Author Mike X Cohen, PhD is a former neuroscience professor, full-time educator, and Udemy bestselling instructor with 25 years of experience teaching machine learning, mathematics, and data science. Other books by Mike X Cohen: - Linear Algebra: Theory, Intuition, Code - Modern Statistics: Intuition, Math, Python, R - Calculus Unraveled: Intuition, Proofs, and Python <img width="555" src="https://m.media-amazon.com/images/I/81tmONmAReL._SY522_.jpg" />
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matched fp:b25074b7d727e789, llm:Repository contains code and materials for the book '50 ML projects to understand LLMs' — educational/learning resources focused on machine learning for large language models.
matched fp:b25074b7d727e789, llm:Repository contains code and materials for the book '50 ML projects to understand LLMs' — educational/learning resources focused on machine learning for large language models.
matched fp:b25074b7d727e789, llm:Repository contains code and materials for the book '50 ML projects to understand LLMs' — educational/learning resources focused on machine learning for large language models.
matched fp:b25074b7d727e789, llm:Repository contains code and materials for the book '50 ML projects to understand LLMs' — educational/learning resources focused on machine learning for large language models.