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This course introduces the fundamental concepts of Natural Language Processing (NLP) and advanced language model technologies. Students will learn through hands-on practice, starting from basic text processing to advanced language model API utilization and NLP application development. The course emphasizes the use of Large Language Models (LLMs) and prompt engineering, aiming to develop practical skills in applying cutting-edge NLP technologies.

Table of Contents

Lecture Notes

• Week 1: Introduction
  • Week 1 Session 1: Foundations and Evolution of NLP
  • Week 1 Session 2: The Revolution in Modern NLP
  • Week 1 Lab - Introduction to NLP Basics
• Week 2: Basics of Text Preprocessing
  • Week 2 Session 1: Text Preprocessing Fundamentals
  • Week 2 Session 2: Advanced Text Preprocessing and Representation
  • Week 2 Session 3: Korean Text Preprocessing and Tokenization
• Week 3: Fundamentals of Language Models
  • Week 3 Session 1: Introduction to Language Models and N-grams
  • Week 3 Session 2: Advanced Statistical Language Models
• Week 4: Word Embeddings
  • Week 4 Session 1: Introduction to Word Embeddings and Word2Vec
  • Week 4 Session 2: Advanced Word Embeddings
• Week 5: Transformers
  • Week 5 Session 1: Introduction to Transformers
  • Week 5 Session 2: BERT
  • Week 5 Session 3: Practical Implementation and Visualization of Transformers
• Week 6: Understanding LLM APIs
  • Week 6 Session 1: Large Language Model (LLM) Basics and Training Strategies
  • Week 6 Session 2: Introduction to LLM APIs and OpenAI API Usage
  • Week 6 Session 3: Sampling Methods and Text Generation
• Special Lecture: 2024 Nobel Prize in Physics
  • Session 1: Foundational Discoveries in Machine Learning with Artificial Neural Networks
  • Session 2: Deep Learning Evolution and Advanced Neural Network Architectures
  • Session 3: Insights from Interviews
• Special Lecture: 2024 Nobel Prize in Chemistry
  • Session 1: Computational Protein Design and De Novo Protein Engineering
  • Session 2: Protein Structure Prediction Using Artificial Intelligence
  • Session 3: Insights from Interviews
• Week 9: Basics of Prompt Engineering
  • Week 9 Session 1: Introduction to Prompt Engineering and Core Techniques
  • Week 9 Session 2: Advanced Prompting Strategies and Prompt Design Principles
• Week 10: Building LLM-based Q&A Systems
  • Week 10 Session 1: Introduction to LLM-based Q&A Systems
  • Week 10 Session 2: Vector Databases and Embeddings
  • Week 10 Session 3: System Integration and Implementation
• Week 11: Web Application Development Basics
  • Week 11 Session 1: Introduction to Web Development and Flask
  • Week 11 Session 2: Advanced Flask and Introduction to Streamlit
  • Week 11 Session 3: Integrating LLM APIs and Deployment
• Week 12: Controlling and Structuring LLM Outputs
  • Week 12 Session 1: Fundamentals of LLM Output Structuring
  • Week 12 Session 2: Advanced LLM Output Control
  • Week 12 Session 3: Real-World LLM Applications

Projects

• Team Project
• NLP Project Proposal
• Week [n] Project Research Note

About

• Syllabus
• Who made this book?
• CHU AI Department

Learning Objectives

1. Understand the basic concepts and key technologies of NLP and language models.

2. Practice core NLP techniques such as text preprocessing, word embeddings, and transformer architecture.

3. Learn methods to perform various NLP tasks using LLM APIs.

4. Master prompt engineering techniques and apply them to solve real-world problems.

5. Develop skills to design and implement NLP-based web applications.

6. Understand the ethical aspects of LLM utilization and learn methods for developing safe AI systems.

Evaluation

1. Attendance and Participation (10%)

2. Weekly Practical Assignments (30%)

3. Midterm Project (25%)

4. Final Project (35%)

Course Materials

• Lecture Note: https://nlp2024.halla.ai

• GitHub: entelecheia/intronlp-2024

• OpenAI API documentation, Hugging Face documentation, latest NLP-related papers and blog posts

Prerequisites

• Basic Python Programming

• Fundamentals of Statistics and Linear Algebra

Additional Notes

• Personal laptop required as the course is practice-oriented

• Course content may be partially modified to reflect the latest technology trends

Changelog

See the CHANGELOG for more information.

Contributing

Contributions are welcome! Please see the contributing guidelines for more information.

License

This project is released under the CC-BY-4.0 License.