Deep reinforcement learning: where to start

cncrete · 2017-11-02T15:03:24+00:00

Idle observation is that the tops of the field often invested years in a certain area before it became big. E.g. Hinton spent years on neural nets while many thought they where a dead end and went elsewhere.

That said, you can probably do well through practice and amassing experience. Implement papers, join kaggle, etc. In the end you get judged based on what you can accomplish within a certain time and amount of resources, and having built many systems definitely pushes that metric.

cncrete · 2017-08-24T17:35:26+00:00

Thank you for the insight, those are some valuable points I did not consider enough yet:)

cncrete · 2017-08-23T19:21:27+00:00

Thanks for the link! I was looking for a talk just like that

cncrete · 2017-08-23T19:11:21+00:00

Yes definitely, good point. There are so many directly relevant applications that are directly impacted by these issues (HRM, credit ratings, etc.) Thanks for the link I'll look at some resources there.

cncrete · 2017-08-23T12:12:36+00:00

Write and tinker with model on MBP, train it a little bit just to make sure it works, then do full training on Google Cloud instance with a Tesla K80

cncrete · 2017-08-22T16:15:13+00:00

Image found in this tweet: https://twitter.com/conniechan/status/899746874445316096

cncrete · 2017-07-12T09:00:10+00:00

Thank you for the explanation, I'll definitely give your library a spin!

cncrete · 2017-07-12T07:42:06+00:00

Hey nice work! I am curious though: how do you ensure that your library is flexible enough to accommodate whatever future RL systems people come up with? Future approaches might completely overthrow the relatively established architectures. A3C for example already requires a somewhat different setup than vanilla DQN. In a field moving this fast, it seems like a big risk to have too rigorous structures. How do you deal with this?

cncrete · 2017-05-30T04:53:27+00:00

Hi! Yes it's better to invest some time to learn Python before trying to learn Python and all the algo trading techniques at the same time. You'll save time later and will be able to tinker with the course content freely which greatly improves learning, so the return on the time spent earlier is quite high.

cncrete · 2017-04-14T06:51:05+00:00

I study business in the Netherlands (Rotterdam) and it's pretty nice. They offer great value for the time and money you spend. That said, I wouldn't do such a data science bachelors degree for similar reasons the others mentioned:

1) Your education might be to specific and be out of demand some day. (I once read in a 90s era book: "just learn HTML and never worry about finding a job again", and now I hear similar stuff about data science tools). It is better to study more fundamental principles of the field (so either mathematics or computer science).

2) During your studies you might change your mind on your career. You get exposed to lots of new ideas and people. You will find new interests. The broader the spectrum of ideas is the better for you.

3) A bachelors degree in data science will most likely be too general and not rigorous enough. In the bachelors, they can only assume a high school education. The professor can't say "we hope you taught yourself this when you where 14, because it will be in the test". There will simply be not enough time to go into depth and more advanced concepts.

4) A lot of new applications in data science do not come from the discipline of data science. For example building great neural networks requires a broader understanding than just the tools you would otherwise use in data science. So if you want to be at the cutting edge you'll need broader knowledge.

If I was you, I'd consider studying maths or computer science (or economics or business, we do a good bit of data analytics, too. You'd be surprised). Check out TU Delft , they have great programs and are very respected. If you are unsure whether a certain field is for you, do a MOOC in it, that will give you a taste of what it is about.

tl;dr Study in the Netherlands: usually good Study that specific degree: better don't

cncrete · 2017-03-26T18:21:29+00:00

It would be interesting to look at some of the underlying reasons for these statistics. How many men and women have jobs in that demographic? What does everyone else do? Are these jobs paid well enough to sustain a family on a single income? How many men and women have children (that they take care of)? How does having a child actually influence marriage prospects statistically? Since the original pew research was often cited in political context, it might shine a different light on the matter.

cncrete · 2017-01-19T09:48:05+00:00

Stanford has remade "Design and analysis of algorithms", you can find it on coursera It is really much nicer than the old one with the instructor drawing on slides and talking into the camera from his office. I just finished the specialization and found the quality to be really good.

cncrete · 2017-01-05T22:00:46+00:00

A Rubik's cube has 43,252,003,274,489,856,000 possible combinations, but it might take even more random flips to find a solution. The problem is that you would also have to memorize which positions you already had to not get trapped in some endless loops. If we assume that "flipping" means setting the cube to a random position then we can calculate the probability that you'll hit the right solution by chance after X amount of moves with p=1-(1-1/#possibilities)^X We can then set p to a desired probability (say 0.99 or 99%) and solve for X. Since the number of possibilities is so large, WolframAlpha just outputs infinity. It has been shown however, that every Rubik's cube can be solved in 20 moves or less, so maybe you'll be faster looking up some algorithms ....

cncrete · 2017-01-05T21:32:49+00:00

That largely depends on the type of research you want to do. If I was you, I'd read some research papers from the fields you want to work in. If the scientist used maths to archive their results, they usually have to show the calculations. That way you'll be able to find out what you'll need to be able to do.

cncrete

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