You might have shared the same frustrations with me when seeing new apps coming up on Android or iOS that are exactly what you wanted to do, but didn’t or never have time to make it real. It’s a bitter-sweet kind of mixed feeling; I’m happy that what I wanted comes true (done by others, though) and…. I am a user, but on the other hand, in my head it keeps saying “That should have been me!”.
So today I suck it up, forget the regrets, and get started. There are tons of good resources on Android and iOS app development on the Internet. Among them I found a few that are extremely good, and I just thought they might be useful for others as well, so let me share them with you.
Building mobile applications — Computer Science CS-76 (provided by Harvard)
URL: [url] You can find good quality course videos and all the materials there.
Previous years archives are also available in iTune.
CSSE490 Android Development Rose-Hulman Winter 2010-2011
Creative Programming for Digital Media & Mobile Apps
This course is not available yet, but just by looking at the syllabus I think the content is essential, yet pretty unique.
The course is coming soon on Coursera.
If you forgot your admin (root) password in Ubuntu. Here is a very comprehensive instruction to reset the password.
I found an awesome tutorial page explaining how to recognize the music interval by ear. The best thing on the page is the table discussing the feeling of each interval.
Today I just found an interesting website BigML, and it seems to offer a playground for people, especially ML researchers, to experiment standard machine learning techniques on your data set or even on your business.
The main website is here:
You can try the BigML for free in development mode, but I think 1 MB for training data set is pretty restrictive though.
MapReduce is a framework to efficiently process a task that can be parallelized using cluster or grid. A good introduction can be found in the link below.
In a sense, MapReduce framework is very similar to message-passing algorithm in graphical models where the Map and Reduce are comparable to building (tree) structure and marginalization of the messages respectively. So, I think MapReduce can make an inference plausible for large-scale graphical models.
Information theory, pattern recognition, and neural networks
Draft videos (not yet edited):
There are some fascinating seminars sponsored by UW, and most of them are recorded:
Every Tuesday 3:30 pm
Yahoo! Machine Learning Seminar
Every Tuesday from 12 – 1 pm
UWTV: Research/Technology/Discovery Channel
Broadcast all the new findings, research, technology for free!!