## Genetic Algorithms, part 2

Last time, in case you missed it, I left off with a laundry list of things I wanted to expand on with Genetic Algorithms (GA). Let’s see which of those I can do this time!

This is pretty wordy and kind of dry, since I was just messing around and figuring stuff out, but I promise the next one will have some cool visuals.

## Mountain car, Q-learning, and Experience Replay with Pytorch

Hey there!

Mountain Car (MC) is a classic Reinforcement Learning (RL) problem. It was briefly shown in a video I was watching, so I figured I’d give it a shot.

## Using Reinforcement Learning to solve the Egg drop puzzle

So last time, I solved the egg drop puzzle in a few ways. One of them was using a recent learn, Markov Decision Processes (MDP). It worked, which got me really stoked about them, because it was such a cool new method to me.

However, it’s kind of a baby process that’s mostly used as a basis to learn about more advanced techniques. In that solution to the problem, I defined the reward matrix and the transition probability matrix , and then used them explicitly to iteratively solve for the value function v and the policy p. This works, but isn’t very useful for the real world, because in practice you don’t know  and , you just get to try stuff and learn the best strategy through experience. So the real challenge would be letting my program try a bunch of actual egg drops, and have it learn the value function and policy from them.

## RPi camera, part 3: a few incremental fixes

Round 3! Okay, this is where I try and polish it up in a couple ways.

Here are the things I said last time I needed to make better:

## 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.

## 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).

## 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.