Notes on codes, projects and everything
With most of my stuff more or less set, I guess it is time to start documenting the steps before I forget. So I heard a lot of good things about docker for quite some time, but haven’t really have the time to do it due to laziness (plus my relatively n00b-ness in the field of dev-ops). Just a few months ago, I decided to finally migrate away from webfaction (thanks for all the superb support) to a VPS so I can run more things on it.
Back then, when I was still working on my postgraduate degree research, I used RDF, which was the preferred format in the world of Semantic Web to represent data. I eventually dropped the degree, and stopped following the development of the related technology and standards. Until I volunteered to update the import script for popit when I was looking for the next job/project.(more…)
As the name implies, Resource Definition Framework, or RDF in short, is a language to represent information about resources in world wide web. Information that can be represented is mostly metadata like title (assuming the resource is a web-page), author, last modified date etc. Besides representing resource that is network-accessible, it can be used to represent things that cannot be accessed through the network, as long as it can be identified using a URI.
I need a slide show script for my portfolio pages but couldn’t find a good one anywhere so I decided to write one myself. The slide show script will be able to display image and the respective description in a predefined order. However, in this version, visitors would not be able to directly jump to a particular slide yet. The script is written in prototype‘s object-orientation approach hence you need to have prototype called.
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.
This post is purely based on my own speculation as there’s no experiment on real-life data to actually back the arguments. I am currently trying to document down a plan for my experiment(s) on recommender system (this reminds me that I have not release the Flickr data collection tool :/) and my supervisor advised to write a paragraph or two on some of the key things. Since he is not going to read it, so I might as well just post it here as a note.