Notes on codes, projects and everything
So I first heard about Panda probably a year ago when I was in my previous job. It looked nice, but I didn’t really get the chance to use it. So practically it is a library that makes data looks like a mix of relational database table and excel sheet. It is easy to do query with it, and provides a way to process it fast if you know how to do it properly (no, I don’t, so I cheated).
The Nand2Tetris part I at coursera is very much my first completed course. It was so fun to actually work through the material and it feels amazing to know how simple it is to actually build a computer from scratch. While it is simple, it doesn’t mean the course itself is easy though. I was struggling to get the CPU wired up properly that I spent two to three days just to get it working.
Writing a usable form and database library has always been a painful experience. So why bother re-inventing the wheel when there are so many to choose from already? I am writing one mostly for learning purpose. After numerous attempts, I finally get my form and database library in shape. It is nowhere complete, but nor it is perfect, but it is currently the implementation that is closest to my original design. I will keep working on it so it can be used in my personal projects in the future.
While working on a text classification task, I spent quite some time preparing the training set for a given document collection. The project is supposed to be a pure golang implementation, so after some quick searching I found some libraries that are either a wrapper to libsvm, or a re-implementation. So I happily started to prepare my training set in the libsvm format.
There are a lot of things I want to post to both here and my personal blogs. However I was sucked into sanctuary for the most of last month. I guess after a month of playing, it is probably time to slowly resume my personal projects.
While following through the Statistical Learning course, I came across this part on doing regression with boosting. Then reading through the material, and going through it makes me wonder, the same method may be adapted to Erik Bernhardsson‘s annoy algorithm.
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