Notes on codes, projects and everything
In the previous post, I re-implemented Annoy in 2D with some linear algebra maths. Then I spent some time going through some tutorial on vectors, and expanded the script to handle data in 3D and more. So instead of finding gradient, the perpendicular line in the middle of two points, I construct a plane, and find the distance between it and points to construct the tree.
Recently I switched my search code to Annoy because the input dataset is huge (7.5mil records with 20k dictionary count). It wasn’t without issues though, however I would probably talk about it next time. In order to figure out what each parameters meant, I spent some time watching through the talk given by the author @fulhack.
Implementing a Information Retrieval system is a fun thing to do. However, doing it efficiently is not (at least to me). So my first few attempts didn’t really end well (mostly uses just Go/golang with some bash tricks here and there, with or without a database). Then I jumped back to Python, which I am more familiar with and was very surprised with all the options available. So I started with Pandas and Scikit-learn combo.
After shifting all my instant messaging accounts to my Nokia N9, I stopped getting email alerts via Adium. Therefore, when I finally remember to check my mailboxes, they are already loaded with exploding amount of mails (mostly junk and newsletter though). I don’t fancy doing my email stuff with my device, and don’t feel like installing a webmail checker to my browser, hence this simple little script is written for my phone.
This update took me quite a bit more time than I initially expected. Anyway, I have done some refactoring work to the original code, and thought it would be nice to document the changes. Overall, most of the changes involved the refactoring of function names. I am not sure if this would stick, but I am quite satisfied for now.
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.
After a miserable trip back to academic world, I finally re-gained the courage to get back to job-market. For the time spent in university, I spent quite some time reading about Semantic Web and RDF. Then I thought, I should have published more in this format in future. However, that didn’t really happen, mostly because I am too lazy.
I am currently preparing myself in applying a postgrad programme and is looking for a research topic. At first I wanted to do something that is related to cloud computing but after some discussion with people around me, they suggest me to do something on semantic web. While posting my notes here, I realized that I had posted something on semantic network that looks like the base of semantic web here (Post still “Under construction” as of writing, will post the diagrams later tonight).