Courses

Teaching Courses

[Undergraduate] Fundamentals of Artificial Intelligence, Guangzhou University, Fall 2025.

Graduation Projects

[Undergraduate] Research on Backdoor Evaluation of Pre-trained Language Models,
Mr. Tao Zhang, Guangzhou University, 2025 [link]

Useful Links

[AI Fundamentals] 3Blue1Brown's Tutorial Videos
But what is a neural network? | Deep learning chapter 1 [link]
Gradient descent, how neural networks learn | Deep learning chapter 2 [link]
Backpropagation, intuitively and calculus | Deep learning chapter 3 and 4 [link]
Large Language Models explained briefly [link]
Transformers (how LLMs work) explained visually | Deep learning chapter 5 [link]
Attention in transformers, step-by-step | Deep learning chapter 6 [link]
How might LLMs store facts | Deep learning chapter 7 [link]

[AI Fundamentals] Hung-yi Lee's Tutorial Videos
Technological breakthroughs and future development of Generative AI | Machine Learning in the Era of Generative AI chapter 1 [link]
How AI Agents work | Machine Learning in the Era of Generative AI chapter 2 [link]
The inner workings of the LLM | Machine Learning in the Era of Generative AI chapter 3 [link]
Introducing Transformer's competitors | Machine Learning in the Era of Generative AI chapter 4 [link]
LLM training method | Machine Learning in the Era of Generative AI chapter 5 [link]
Training LLMs using multiple GPUs [link]
Post-training and forgetting problems in Generative AI | Machine Learning in the Era of Generative AI chapter 6 [link]
How DeepSeek-R1 performs Reasoning | Machine Learning in the Era of Generative AI chapter 7 [link]
The Reasoning process of a LLM does not need to be too long | Machine Learning in the Era of Generative AI chapter 8 [link]
Evaluation of LLMs | Machine Learning in the Era of Generative AI chapter 9 [link]
A brief discussion on Model Editing | Machine Learning in the Era of Generative AI chapter 10 [link]

Preface | Introduction to Generative AI chapter 0 [link]
What is Generative AI? | Introduction to Generative AI chapter 1 [link]
What is so powerful about today's Generative AI? From "Tool" to "Tool Person" | Introduction to Generative AI chapter 2 [link]
Can't train AI? You can train yourself: Magic spells and more information | Introduction to Generative AI chapter 3 [link]
Can't train AI? You can train yourself: Problem breakdown and tools | Introduction to Generative AI chapter 4 [link]
Can't train AI? You can train yourself: Let languages cooperate with each other and turn a person into a team | Introduction to Generative AI chapter 5 [link]
Phases of LLM Training - Phase 1: Self-study and accumulation of strength | Introduction to Generative AI chapter 6 [link]
Phases of LLM Training - Phase 2: Guidance from famous teachers, unleashing potential | Introduction to Generative AI chapter 7 [link]
Phases of LLM Training - Phase 3: Participate in actual combat and hone the skills | Introduction to Generative AI chapter 8 [link]
AI Agent built with LLMs | Introduction to Generative AI chapter 9 [link]
How do today's LLMs play word solitaire? — A brief talk on Transformer | Introduction to Generative AI chapter 10 [link]
What are LLMs thinking? — A brief talk on the interpretability of LLMs | Introduction to Generative AI chapter 11 [link]
A brief talk on various ways to test the capabilities of LLMs | Introduction to Generative AI chapter 12 [link]
A brief talk on security issues related to LLMs: Part 1 | Introduction to Generative AI chapter 13 [link]
A brief talk on security issues related to LLMs: Part 2 | Introduction to Generative AI chapter 14 [link]
A brief talk on the generation strategy of Generative AI | Introduction to Generative AI chapter 15 [link]
A magical plugin that can speed up the generation of all LLMs — Speculative Decoding | Introduction to Generative AI chapter 16 [link]
Generative AI for Imaging — How AI generates pictures and videos | Introduction to Generative AI chapter 17 [link]
Generative AI for Images — A quick guide to classic image generation methods (VAE, Flow, Diffusion, GAN) | Introduction to Generative AI chapter 18 [link]

[AI Theory] Jianlin Su's Blog - Scientific Spaces [link]

[Research] Suggestions from Professors
Prof. Srinivasan Keshav - How to read a paper [link]
Prof. Ivan Stojmenovic - The best method for presentation of research results in theses and papers [link]