In the era of autonomous AI Agents,
you can utilize various Agent tools and Public APIs such as OpenAI, Claude, and Codex.
However, in a real service environment, you must also consider
data security, network costs, token costs, and GPU resource management.
Therefore, what is important is
an understanding of the Hybrid AI architecture that combines Public APIs and self-hosted GPU-based LLMs
according to the situation.
In that case, is using only Public APIs always the best option?
Not necessarily.
Nowadays, many LLMs comparable to public APIs (ChatGPT, Claude, Sonnet, etc.)
are being developed both domestically and internationally.
However, knowing and using LLMs well is not easy.
There is a significant difference between
understanding and using LLMs versus using them without understanding,
especially after purchasing expensive GPUs.
(This is the content for Chapter 3-5. Make sure to take away all the remaining key parameters.)
(This is the content for Chapter 5-4. The evolution of Attention is the evolution of LLMs.)
Those who wanted to study Transformer and Attention structures,
but found it difficult due to complex formulas and concepts
Those who have used ChatGPT or generative AI services,
but want to understand the principles of how LLMs actually work
AI engineers who need to understand LLM architecture and GPU environments,
and possess the capability to build and operate actual AI systems
The vLLM service will be hosted on Runpod, but
OpenWebUI and FastAPI will also be running on your local computer.
Therefore, please check if your environment meets the requirements below!
I was surprised by how professional and much more detailed the lecture was than I expected. It feels like it's been a long time since I've seen a lecture of this quality. Since my job involves AI-related engineering, I purchased this thinking I would just roughly study the basics of LLM Architecture, but I am extremely satisfied.
It's very helpful.
I am not in the deep learning industry, but I work in the field of computer vision (rule-based). Since my company requires LLM and vision-related deep learning technologies, I have been studying these topics. I have only completed about 40% of the course, but I felt compelled to leave a review now. I have taken many deep learning courses, including those by famous and highly-rated instructors, but I haven't found any course as clean and clear as this one. The best part is the quality of the lecture materials. The instructor recorded every single matrix calculation in Excel, which is incredibly helpful when reviewing. The Python code is also well-commented in many places. The quality of the lectures themselves is excellent; the instructor reminds you of parts you might have forgotten, ensuring you don't miss anything. While most other courses show calculations once or twice and then move on, this course goes through the calculations together until the end, which provides great clarity. It seems like the Q&A is monitored frequently, as I received immediate answers to my questions. The lectures seem to have been filmed this year, so it's great that they include a lot of the latest trends. It feels like this course hasn't gained much word-of-mouth yet, but I highly recommend it to anyone who needs to study these topics.
It's good for the subject matter it covers or as an introduction.
It's challenging, but I'll do my best to follow along. I like that the content contains only the essential core information.
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