Exploring Generative AI: A Deep Dive into LLMs

Introduction to Generative AI

As the field of artificial intelligence rapidly evolves, understanding the basics of large language models (LLMs) has never been more important. One resource that stands out in this educational journey is the course titled “Introduction to Generative AI – Spring 2024” by Professor Hongyi Li from National Taiwan University. This course has proven to be a fantastic starting point for those looking to grasp the nuances of generative AI.

📍 Course Official Website: 👉 Click Here (You can also find several resourced materials on various platforms.)

Course Style and Accessibility

One of the most appealing aspects of this course is its friendly approach to teaching. Whether you have no background in machine learning or deep learning, you’ll find this course accessible and enriching. Professor Li has a knack for using relatable examples and includes animations to clarify complex concepts, making them easier to grasp.

With comprehensive resources, each chapter comes with downloadable, editable PPTs 📑 and hands-on assignments. The teaching assistants also provide detailed explanations, further enhancing the learning experience.

Additionally, the course includes links to extra materials covering pre-requisite content such as Transformers, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and more. The thorough structure of the course makes it a well-rounded option for learning AI.

A Systematic Approach to Learning LLMs

While the course may not contain the latest state-of-the-art (SOTA) advancements in 2024, it serves as an excellent introduction to generative AI, providing a solid foundation for understanding LLMs. By the end of the course, you will have a systematic concept and strategy for further exploration in the field. Each section recommends relevant papers for deeper insights and study.

Core Content Overview

• What is Generative AI? – Understand the main LLMs, including GPT, Gemini, and LLaMA, along with their parameter sizes, training data, and iterative processes.
• LLM Functionality and Enhancement Methods: – Learn about prompt engineering techniques, fine-tuning, and small-scale training.
• Training Phases: – Explore pre-training with massive datasets, fine-tuning with supervised learning, and reinforcement learning with human feedback (RLHF).
• Agent Systems: – Delve into single and multi-agent collaboration and task execution.
• Model Principles: – Gain insights into the Transformer model, stripped down to highlight the Attention mechanism without RNN steps.
• Interpretability & Safety: – Investigate methods like probing, embedding projections, and strategies to mitigate biases and hallucinations.
• Comparison of Generation Methods: – Text generation via autoregressive (AR) methods and image/video/audio via non-autoregressive (NAR) or AR+NAR combinations.