Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A complete roadmap to master LLMs from absolute beginners to advanced
| Date | Stars |
|---|---|
| 2026-07-31 | 456 |
| 2026-08-06 | 462 |
Today
+6 stars today
This week
— stars this week
This month
— stars this month
Momentum
9.0
growth rate 0.00%/day
# LLM-Open-University-From-Begineer-to-Advanced A complete roadmap to master LLMs for absolute beginners to advanced [](https://youssefh.substack.com/) [](https://medium.com/@yousefhosni) [](https://www.kaggle.com/youssef19) [](https://www.youtube.com/channel/UCeEcSgRzYFuVt-2Yk1ULdhQ) > 📘 **Support this project** > This repository is free and open source. The same content is also available as a published book: **LLM Roadmap: From Beginner to Advanced**. > If you want to support the work behind this roadmap, you can buy the book version. [](https://youssefhosni.gumroad.com/l/qigmtg) Large Language Models (LLMs) are now an important part of modern AI systems. They are used in chatbots, search systems, coding assistants, data analysis tools, agents, and many other applications. Because of this, LLM-related skills are becoming increasingly important for data scientists, machine learning engineers, AI engineers, and software developers working in the AI field. This repository provides a structured roadmap for learning LLMs from beginner to advanced level. The goal is to help you understand the core concepts, learn how LLMs are built and adapted, and practice building real applications with them. The roadmap is divided into four main sections. The first section covers the foundations of Large Language Models, including LLM architecture, transformers, attention mechanisms, tokenization, embeddings, and other core concepts needed to understand how these models work. The second section focuses on building and training LLMs. It covers dataset preparation, fine-tuning, evaluation, quantization, alignment techniques such as RLHF, and the importance of staying updated with new model releases and research. The third section moves from model understanding to application development. It covers prompt engineering, vector databases, Retrieval-Augmented Generation (RAG), running LLMs locally, deployment, inference optimization, LLMOps, and security considerations for production systems. The final section is focused on portfolio building. It includes project ideas and guided projects that can help you apply what you learned and demonstrate your skills through practical work. Each section builds on the previous one, moving from fundamentals to training, then to production applications, and finally to portfolio projects. By the end of the roadmap, you should have a clear learning path and a practical understanding of how to work with LLMs across different stages of the development lifecycle. <img width="1672" height="941" alt="ChatGPT Image Jun 6, 2026, 06_24_22 PM" src="https://github.com/user-attachments/assets/c35b2b4a-2d0a-4349-9d7f-09476198774b" /> ### Table of Contents: ### - [Part I: LLM Basics & Architecture](#part-i-llm-basics--architecture) - [1. Articles](#1-articles) - [2. YouTube Videos](#2-youtube-videos) - [3. Courses](#3-courses) - [Part II: Building & Training LLM From Scratch](#part-ii-building--training-llm-from-scratch) - [1. Best Resources on Building Datasets to Train LLMs](#1-best-resources-on-building-datasets-to-train-llms) - [2. Practical Guide to LLM Fine-Tuning](#2-practical-guide-to-llm-fine-tuning) - [3. Best Resources to Learn & Understand Evaluating LLMs](#3-best-resources-to-learn--understand-evaluating-llms) - [4. Overview of LLM Quantization Techniques](#4-overview-of-llm-quantization-techniques--where-to-learn-each-of-th
Excerpt of 121,801 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:359d3be7da60ee5f, desc:roadmap