I have always wondered how information analysis works in government intelligence agencies or corporate business intelligence departments. A few weeks ago, I saw someone recommend Psychology of Intelligence Analysis, so I read through it with the help of LLM tools. It’s an interesting but somewhat verbose book that, in my opinion, could be well-summarized in two or three articles.

Performance optimization of LLM systems requires a thorough understanding of the full software stack. Somehow I couldn’t find a comprehensive article that covers the big picture yet, so instead of waiting for one, I decided to write this article. This article is not a comprehensive review or best practice guide, but rather a sharing of my overall perspective on the current LLM system landscape.

Disclaimer: This blog was originally published on OmniML’s website in November 2022. Since the release of ChatGPT, ML industry has greatly shifted priorities and a lot of the previous assumptions have changed. Neverthless, I still kept the blog for historical reference to the pre-LLM-era view of ML systems.

You may have wondered how autograd actually works in imperative programming. In this post, I am going to explain it with hand-by-hand examples. Unlike other tutorials, this post is not borrowing one single line of codes from PyTorch or MXNet, but instead building everything from scratch.