EDX Artificial Intelligence, weeks 2 and 3

Hiya!

I started this AI course with my friends a while ago, but we never ended up finishing it. I’m interested in AI these days, so I thought I’d try it on my own. Week 1 is some fluff that’s not worth going over. I’m doing weeks 2 and 3 together because there is one project for both combined. The first couple sections are just notes I took on the videos and concepts. The stuff for the project is at the bottom. read more

Snackin my way across Viet-nom nom nom

This will proooobably have to be a two-parter. I spent the most time of any country in Vietnam, and my other posts probably should’ve been two parts themselves… Also, I’m gonna try a new format, with sections! Lawdy, at this point, it’s been a while, so I’m struggling to sit down and write it all down before I forget it.

  1. Getting into Vietnam, friends, and Hanoi
  2. Ninh Binh
  3. Cat Ba Island and Ha Long bay
  4. The Ha Giang Loop
  5. Motorcycles and setting off

Hanoi read more

Motion detection with the Raspberry Pi, part 1

Okay Declan, let’s try making this post a short and sweet update, not a rambling Homerian epic about simple stuff.

I got a Raspberry Pi (RPi) and an RPi camera because I wanted to learn about them and mess around with them. If I could do image recognition with them, that’d be a good platform to do ML, NN, and if I got enough data, maybe even DS type stuff. Luckily, there’s a ton of resources and code out there already. I drew upon heavily from www.pyimagesearch.com, which is a REALLY useful site, explained very great for beginners. Two articles that I basically copied code from and then butchered were this and this. read more

Kaggle Housing challenge, my take

In this article, I’m doing the Kaggle Housing challenge, which is probably the second most popular after Titanic. This was very much a “keeping track of what I’m doing for learning/my own sake” thing, but by the end I’ve gotten a ranking of 178/5419 on the public leaderboard (LB). That said, this is super long because I tried a million things and it’s kind of a full log of my workflow on this problem.

I’ve really learned a bunch from going through this very carefully. What I did here was to try the few techniques I knew when I started, and then I looked at notebooks/kernels for this challenge on Kaggle. A word on these kernels: even the very most top rated ones vary in quality immensely. Some are excellently explained and you can tell they tried different things to try and get an optimal result. Others are clearly people just trying random stuff they’ve heard of, misapplying relatively basic techniques, and even copying code from other kernels. So I viewed these as loose suggestions and guideposts for techniques. read more

The Knapsack Problem: Discrete Optimization, week 2

I’ve been doing this Coursera Discrete Optimization course with my friends. It’s a lot of fun and I’m learning a bunch. The instructor is a total goofball too, which is a plus. I’ve taken a handful of online courses before, but to be honest, the assignments have usually been pretty easy. Not so with this Discrete Optimization (DO (my initials!)) course! Each week, you have to solve 6 problems, and each is graded out of 10 depending on how well you do. I believe the breakdown is: 0/10 if your answer doesn’t even match the output format required, 3/10 if you do basically anything (even if your answer is quite wrong), 7/10 if you have an answer above some threshold but still not perfect, and 10/10 if your answer is optimal (I guess they know the optimal solution for all of them?). Usually, the problems increase in hardness throughout the set; often, the last one is difficult enough that (I believe we saw this said in the course forums by the instructor) it would be a challenge for people who specialize in DO for a living. I think that’s pretty cool! They usually give you a ton of practice problems of various difficulties, and (though I’m not 100% sure) I think the 6 you’re graded on are usually among those.

So what is DO? I certainly didn’t know when I started this course, though I guess I should’ve been able to guess. Optimization is what it sounds like, finding the best solution you can for given problems. The “discrete” part is that the quantities involved are integers or discrete (that’s the name in the title!!!) components. It turns out I had actually heard of many of the problems that DO applies to before, but didn’t know they were DO. I had heard most of them in the context of P vs NP complexity. read more

Fruits of south east asia!

I forget how I found it, but for some reason I stumbled upon this page of Thai (or more generally, south east Asian) fruits. A bunch of them are really obvious ones (mango, banana, coconut…), but a handful of them are ones I’ve never even heard of. Naturally, I have to try them all.

I mean, isn’t that kind of weird? I like to think I’ve at least heard of most of the major fruits, vegetables, and animals. I don’t mean that I’ve heard/tasted/seen literally all of them, but I usually think that the ones I haven’t heard of are just some small variant of one I already know. Like, if you learn about a blood orange, it’s cool, and yeah, a little different… but not mind blowingly different than a regular orange. It looks about the same aside from the red flesh, and tastes pretty similar. If you hear about moon bears, they’re…basically kinda weird black bears. You get the point. read more

Reading a book in one hour!

When you’re unemployed, you get to do all sorts of things that, if you had a job, you’d correctly judge as stupid, and then not do. Here’s one of them!

I was curious as to how much information I can pick up in an hour. I mean, I’ve gone to lots of talks, but I think a lot of the time it’s because they’re pretty specific, advanced topics (I mean, they’re usually talks about someone’s research). So I don’t think they’re necessarily the best metric for that. Part of why I’m curious is that there’s gotta be some sort of “information retention vs time spent learning it” curve, and I don’t really have a great grasp of the shape of it. I mean, I’m pretty sure that it’s monotonically increasing with time, but I really don’t know where the best ratio of it is. read more

Grouping IMDb top movies by runtime

Howdy!

This is a fun lil one. For an upcoming article, I need to know a list of (hopefully good) movies I haven’t yet seen, with similar runtimes. Now, I could have just scrolled down the list of IMDb.com’s top 250 movies, ctrl + clicking on the ones I haven’t seen, and then compared them by eye, because, to be honest, I think I’ve seen many (/most?) of them (we’ll see shortly). read more

Stickin it to the Myan-mar

Ahhhhhhhhh, Myanmar. “You most likely know it as Myanmar, but it’ll always be Burma to me.”

While I originally planned to go to Thailand, Laos, and Cambodia, and maybe Vietnam, I really didn’t expect to go to Myanmar at all. To be honest, pretty much all I knew about it was that line from Seinfeld and that there’s currently a genocide/ethnic cleansing/refugee crisis happening with the Rohingya in the west of the country being committed by the Myanmar government (more on that later). However, I kept meeting people who told me that it was the highlight of their whole trip in SEA. When I had a few weeks to kill before meeting my friends in Vietnam, since I had kind of tired of Cambodia, it seemed like the perfect opportunity. read more

Dimensionality reduction via Principle Component Analysis in python on face images

Hey there! It’s been a while since I wrote anything other than stuff about travel (oh, don’t you worry, there’s still more of that coming!), so it feels good to write about something like this.

Right now, I’m almost finished with the Andrew Ng Machine Learning course on Coursera. Maybe I’ll write about it sometime, but it’s really, really solid and I’m learning a lot. He’s pretty great at explaining concepts and the course is constructed pretty well. What I really like is that, for the assignments, he’ll take the concept from that week and demonstrate a really interesting application of it (even if it’s a little contrived and may not actually be a practical use for it). Either way, it just gets me to think about the breadth of what this stuff can be applied to. read more