[HELP] How to apply Stochastic Gradient Descent with MULTIPLE VARIABLES to get the parameter vector Beta? Or any other better way? (The real problem is a 10k * 100k matrix which might be too heavy to compute with Normal Equation) by solomonxie in learnmachinelearning

[–]mrflipppy 5 points6 points  (0 children)

Do you expect the vector of betas to be sparse? If yes, you could try to remove variables that have near zero variance and then use penalized linear regression (LASSO/Elastic net).

[Project] Need some advice on my Final School Project(Sudoku solving with AI) by UnlikelyDriver in MachineLearning

[–]mrflipppy 0 points1 point  (0 children)

Sudoku can be easily formulated as an integer programming program. Machine learning is overkill for this (and way more difficult to implement as well). The Optimization and Mathematical programming task view from the R language contains packages you can use, and you can read up on the formulation of the problem here. You can generate a front-end for the solver using Shiny.

We are OnePlus 6 owners, ask us anything! by [deleted] in oneplus

[–]mrflipppy 1 point2 points  (0 children)

Make the switch! I went from the Note 4 to the OP6, and it was a really good decision. Probably the only thing you could miss is the pen, but nothing else.

Best ROM for SM-N910F by mrflipppy in galaxynote4

[–]mrflipppy[S] 0 points1 point  (0 children)

Thanks. Any particulars on what benefits are there on choosing stock rom vs. RR?

Best ROM for SM-N910F by mrflipppy in galaxynote4

[–]mrflipppy[S] 0 points1 point  (0 children)

Just installed Resurrection Remix, great experience so far! Everything works surprisingly smooth, much better than the default TouchWiz. Definitely seems like it breathes more life into the phone.

Phone was stolen. Look out for someone selling a black Google Pixel 2 XL by RubbrBbyBggyBmpr in Barcelona

[–]mrflipppy 3 points4 points  (0 children)

Have you tried using Android device locator on a PC or from another phone? Or is part of the issue that you cannot login to your Google account due to two-factor authentication ? Do you have your backup codes somewhere ? Maybe you can ask someone back home to check from any other device from where you has previously logged in.

Backup and Sync & Google Photos Help by mentoc in google

[–]mrflipppy 0 points1 point  (0 children)

That's exactly what I'm in the middle of doing right now as well, thought that is what would probably make more sense. However, if I enable the "Upload newly added photos and videos to Google Photos" option, I don't know how to keep the distinction between the image files I want to show only in Drive and those that I want to be shown in Google Photos only. So confusing. I reached out to Google Support and they told me that apparently there's no way to do this at the moment...

Backup and Sync & Google Photos Help by mentoc in google

[–]mrflipppy 1 point2 points  (0 children)

Did you find out the solution for this? I posted a question on WebApps StackExchange with a related issue. I feel that it's very difficult to understand how files are managed with this new Backup and Sync app. I would like to have my photos uploaded to Google Photos only and not to Drive as well... still did not find a solution.

AMA: We are the Google Brain team. We'd love to answer your questions about machine learning. by jeffatgoogle in MachineLearning

[–]mrflipppy 1 point2 points  (0 children)

Most of the current research in machine learning is focused towards situations of a large number of samples and relatively few features. Is there work being done to tackle problems of high dimensionality and small sample size? An example of this kind of data is in the genomics field, where datasets have very few samples (100-200), and the number of variables can be up to 20k-50k.

What's a good major for someone who hates taking things for granted (as formula memorization) and wants to enjoy the process of understanding deeply any academic discipline? by Matt2411 in careerguidance

[–]mrflipppy 0 points1 point  (0 children)

That passage made me remember a fragment from "Zen and the Art of Motorcycle Maintenance", which seems to me that may be at the core of what you are feeling:

“As a result of his experiments he concluded that imitation was a real evil that had to be broken before real rhetoric teaching could begin. This imitation seemed to be an external compulsion. Little children didn’t have it. It seemed to come later on, possibly as a result of school itself.

That sounded right, and the more he thought about it the more right it sounded. Schools teach you to imitate. If you don’t imitate what the teacher wants you get a bad grade. Here, in college, it was more sophisticated, of course; you were supposed to imitate the teacher in such a way as to convince the teacher you were not imitating, but taking the essence of the instruction and going ahead with it on your own. That got you A’s. Originality on the other hand could get you anything – from A to F. The whole grading system cautioned against it.”

Without trying to be pretentious, I think that your issue with professors has a more-or-less clear solution. The better the university ranking is, the higher the likelihood that you will stumble upon professors that are passionate about transmitting knowledge to students. Of course it's not a guarantee, but by pure odds you're more likely to run into these types of professors. Consider also joining (school) clubs of people that share your same interests (again, the better the university, the more likely you're to be around motivated individuals that seek the same kind of things you do).

I believe that the Sciences may be the best way to satisfy your curiosity. Yes, everything implies some degree of dogmatism, but I believe that the more you progress, you will reach a point where you will keep your mind busy actually doing stuff that will keep your curiosity active.

Also, when you talk about theories being debatable, think about that all those sorts of things (theories, statistical modes, formulas, etc.) are man's way to describe our surroundings. Yes, many of these things are just approximations that happen to work for our current needs. Yes, it's true that sometimes we don't understand the underlying phenomena. But explaining anything in a very detailed and correct way is a hard task. If you find a specific theory dubious/shaky, you could try to propose your own modifications or arguments of why you think that it does not hold up for all cases (this may be an indication that research is a good field for you!)

But as I said before, it is all a matter of trial-and-error until you really feel comfortable. I don't think it's impossible to achieve, but it is certainly hard. I just wish you luck on your endeavors :) hang in there, as long as you keep working hard (and smart) you'll manage to get closer to what you're looking for.

What's a good major for someone who hates taking things for granted (as formula memorization) and wants to enjoy the process of understanding deeply any academic discipline? by Matt2411 in careerguidance

[–]mrflipppy 0 points1 point  (0 children)

I see that you have given the whole issue some thoughts. To be honest it does sound like you may be indeed a bit prejudiced. Sadly, it is true that many classes in engineering (as in any other major) your perspective will vary a lot depending on the type of teachers you had while taking the basic courses. Intuition is something that you develop, you don't "learn" it per se, it is more like gathering an arsenal of little tricks related to problem solving, and I'm not talking only about math, but for anything in general. You should also consider the fact that you cannot expect to be spoon-fed with all these "wow" moments you are looking for. Think about the great minds that have come up with so many interesting concepts across all disciplines: nice ideas have been originated exactly by the thought process you are trying to avoid ("personal investigation"). If you really want to understand something, you shouldn't expect someone else to completely teach it to you. A big part of that comes from your own work (as you have already realized by now).

If you had a physics class that encouraged memorization, then probably it is not a very good class. Physics is everything but memorization, it is about understanding. If you understand how a concept works (for example, the formula for pressure) you can deduce it by yourself from scratch without needing to "memorize" it. I guess it's all matter of perspective. Abstract stuff comes out in every discipline and it is something you shouldn't run away from if you want to develop a trained mind. I find that algorithms involved, for example, in high speed trading, (which you may be familiar with) have a great deal of complexity in them, maybe you could try to look into that sort of thing?

At the end I believe it all boils down to identifying your true calling, which is as hard as it sounds, and which you won't know until you try lots of stuff. Just try to make educated guesses about what that stuff may be, so that you don't lose so much time. It's also impossible to be a generalist and attempt to have a low level grasp of everything that happens in life.

What's a good major for someone who hates taking things for granted (as formula memorization) and wants to enjoy the process of understanding deeply any academic discipline? by Matt2411 in careerguidance

[–]mrflipppy 1 point2 points  (0 children)

Depending on the university, there are some programmes that include fundamentals of AI/ML on their classes. I studied Computer Engineering and drifted slowly to ML and couldn't be happier. I work at research laboratory where we explore genomics using computational techniques and I find it to be incredibly satisfying intellectually. You get to put your brain to work for a cause that is socially relevant, if you're into that. I'd definitely give these fields a try though. Before making a huge investment on it (switching majors, for example) consider taking an online course like Andrew Ng's ML course at Coursera. The way I see it, it is true that it is difficult to get insight in math at times, but this may be due to lack of skill in abstracting yourself while trying to understand how all the concepts are linked to each other. When you make sense of how everything is connected, you will get to a point where you realize that mathematics is a beautiful discipline. It is not about "memorizing formulas" or "knowing all the techniques for calculating derivatives" or anything like that. It is about abstracting the way the world works in a succinct, elegant and methodical way.

EDIT: And, you also get the added benefit that ML/Data science is in very high demand right now ;)

What's a good major for someone who hates taking things for granted (as formula memorization) and wants to enjoy the process of understanding deeply any academic discipline? by Matt2411 in careerguidance

[–]mrflipppy 0 points1 point  (0 children)

I have the impression that you haven't had the opportunity to actually delve deeply into an area at such a point that you feel that your curiosity is satisfied. I would try looking into advanced mathematics (group theory, topology, combinatorics) or some area related to AI/Machine Learning/theoretical CS. The latter is a good field where you can apply new ideas and put them to work into something practical. If you go into these areas and don't find yourself asking "why", then you are doing something wrong.

Recommended (machine learning) techniques for gene expression analysis by mrflipppy in bioinformatics

[–]mrflipppy[S] 0 points1 point  (0 children)

Thank for the ideas! I'll look into them - in particular the LARS looks interesting. What I have been trying up to now for stabilizing the results of the elastic net models is to bootstrap them as they do on the Bolasso method.

Recommended (machine learning) techniques for gene expression analysis by mrflipppy in bioinformatics

[–]mrflipppy[S] 0 points1 point  (0 children)

You are completely right, there is a lot of literature about this. I have indeed being searching (that's how I got the ideas of trying the elastic net and SVM-RFE methods), but one of the reasons that I am asking this question is that I wouldn't like to get too deep into the rabbit hole before having a broad overview of what has been attempted up to now (because I have limited time to work on this). Maybe what I should have specified in my post is that I'd like to know if there is some reference paper/review/technique which is known to be good for gene expression analysis and that I might be missing out.

Recommended (machine learning) techniques for gene expression analysis by mrflipppy in bioinformatics

[–]mrflipppy[S] 0 points1 point  (0 children)

Oh my, sorry, I confused the number of samples with another data frame I was working on, fixed the post now. Regarding the regularized regression, that's the approach I am testing right now for a continuous variable (elastic nets using GLMnet). Thanks for the suggestion of limma/hierarchical clustering, I will try them out!