Hello, students!
Thank you sincerely for your interest in and support of the Deep Learning Ho-hyung course.
It’s been almost six years since I first created the Deep Learning introductory course.
Even after the introductory course, I have continued creating new courses in line with the rapidly changing AI trends. I was also the first in Korea to create a course on Vision Transformer theory and code, and I have covered a variety of topics, including a comprehensive YOLO course and physics-informed neural networks (PINNs).
However, the pace of AI technology’s development over the past few years seems to have been much faster than I anticipated.
Nevertheless, the importance of basic concepts remains unchanged.
You may be using a variety of LLM-based services, including ChatGPT, quite often these days, but ultimately, the depth of your questions and the quality of the answers you can get depend on whether or not you have a basic understanding. I believe that understanding the underlying principles is also important for properly understanding and making use of new AI technologies.
This is something I’ve wanted to add to the introductory course for a long time, and I’ve finally been able to add the new lessons.
The content added this time is an introductory course on the two models that form the core foundation of today’s generative AI: Transformer and Diffusion Model.
I hope this lecture will also help those who have taken the existing introductory lecture understand the progression from the fundamentals of artificial neural networks to recent generative AI technologies.
Thank you.
Best regards, Deep Learning Ho-hyeong