🧠 NLP & Language Models — From Fundamentals to Retrieval-Augmented Generation

Welcome to this comprehensive and structured course on Natural Language Processing (NLP), Language Models (LMs), and Retrieval-Augmented Generation (RAG).
This repository provides a concise yet complete theoretical foundation for understanding modern NLP and large language models — with a focus on clarity, intuition, and references to key works.

---

🎯 Objectives

• Understand the evolution of NLP, from symbolic methods to neural architectures.
• Master the theoretical principles behind modern language models and transformer-based architectures.
• Grasp the core ideas and challenges of RAG (Retrieval-Augmented Generation) systems.
• Build a strong conceptual foundation for applied research or system design.

---

📚 Course Structure

Module	Folder	Topics
1. NLP Fundamentals	01_nlp_basics/	Text preprocessing, feature extraction, evaluation
2. Language Models	02_language_models/	From n-grams to transformers, embeddings, fine-tuning
3. RAG Systems	03_rag/	Retrieval, generation, architectures, evaluation

---

🧩 What’s Inside

Each section is designed as a short course module with:

• Concept explanations and mathematical foundations
• Conceptual diagrams (described textually)
• Historical context and key references
• Connections between classical and modern methods

⚠️ This course is mainly theoretical and does not include executable code.
When useful, we mention tools such as Hugging Face, LangChain, or ChromaDB for context.

---

📖 Recommended Background

• Basic knowledge of machine learning (loss, optimization, overfitting).
• Some familiarity with Python and probability.
• No deep math background required — intuition is prioritized.

---

🧠 Further Reading & References

• Jurafsky & Martin — Speech and Language Processing (3rd ed. draft)
• Goldberg, Y. — Neural Network Methods for NLP
• Vaswani et al. (2017) — Attention Is All You Need
• Lewis et al. (2020) — Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• Bommasani et al. (2021) — On the Opportunities and Risks of Foundation Models

---

🧩 License & Contributions

This content is released under the Creative Commons Attribution (CC BY 4.0) license.
You are free to reuse, adapt, and share with attribution.

Contributions and corrections are welcome — feel free to open issues or pull requests.

---

Author: Adrien BONNEU
Created: 2025
Version: 1.0