07:06 < mulx10> rcurtin: Yeah Sure. Sorry for that.

08:14 < mulx10> zoq: I have a question regarding reinforcement learning idea of GSoC `19

08:14 < mulx10> Should the new code be added in mlpack reinforcement_learning method or gym_tcp_api/cpp ?

08:15 < mulx10> Thank you!

08:23 < ShikharJ> rcurtin: zoq: Yay! :P

09:01 < jenkins-mlpack2> Project docker mlpack nightly build build #247: STILL UNSTABLE in 3 hr 47 min: http://ci.mlpack.org/job/docker%20mlpack%20nightly%20build/247/

09:49 < Here-again> Hello. I have a windows laptop and I cannot run Linux as I have some softwares for college which are only on windows. So can I develop and play with mlpack libs on windows or virtual box is a better option?

09:50 < Here-again> P.s. Mine is a slow PC so virtual box is kinda slow

10:26 < zoq> mulx10: The code should go in the mlpack reinforcement_learning codebase. gym_tcp_api is basically just a tool to visualize the learning process

10:31 < zoq> Here-again: Hello, mlpack works on windows as well, the build process is somewhat slow. See http://mlpack.org/docs/mlpack-3.0.4/doxygen/build_windows.html for more information, https://github.com/mlpack/mlpack/pull/1736 might be interesting as well.

10:32 < zoq> Here-again: Or if you like to use mingw: http://kurg.org/mlpack_mingw_build.html

15:39 < New-bie> I just recently learned ml and want to contribute in gsoc 2019.

15:40 < New-bie> What are issues on traditional ml algorithms which doesn't involve neural networks which I could work on

15:40 < New-bie> I have visited GitHub issues page but couldn't find any

15:40 < New-bie> Specifically can I know issues related to linear regression?

15:42 < rcurtin> New-bie: there are numerous issues on the Github issues list that have to do with machine learning but not neural networks

15:42 < rcurtin> however, I don't think there are any for linear regression specifically

16:34 < KimSangYeon-DGU> sumedhghaisas: Hi sumedhghaisas, My name is Kim SangYeon, I'm a student in Korea and an aspirant for GSoC 2019 and I would like to apply for the 'Quantum Gaussian Mixture Models' project. I think it is very interesting. Currently, I've tried to get accustomed to mlpack's codebase by handling some issues. Thank you all for giving students such a great opportunity. :)

16:39 < sumedhghaisas> KimSangYeon-DGU: Hi

16:40 < KimSangYeon-DGU> :)

16:40 < sumedhghaisas> I am also currently investigating that paper for its correctness. Have you gone through the paper? You will comfortable with the ideas presented?

16:48 < KimSangYeon-DGU> When I read the paper overall(introduction, proposed clustering methods, experiments and tests), It is comfortable to me. I'm going to read the paper more deeply.(checking the correctness, how to convert the equations to mlpack codebase)

16:49 < KimSangYeon-DGU> Sorry for late response, I have to be careful to answer your question.

16:56 < lenixlobo> Hi

16:57 < KimSangYeon-DGU> sumedhghaisas: So interesting. :)

16:58 < KimSangYeon-DGU> lenixlobo: Hi

16:58 < lenixlobo> I am interested in the NEAT implementation in mlpack mentioned in the GSOC ideas list, and was wondering how do i get started

17:00 < rcurtin> lenixlobo: hi there, what have you done to try and figure out what to do?

17:02 < lenixlobo> I have worked with Reinforcement Learning (DQN,DDPG,Actor-Critic) mostly in python, and read the NEAT paper twice now. I would like to get into implementation of the PAPER.

17:07 < lenixlobo> How do i start contributing?

17:07 < mulx10> zoq: Oh okay

17:08 < mulx10> zoq: So can I use the env from gym_tcp_api? Or do I need to write envs as in reinforcement_learning method?

17:10 < mulx10> zoq: thank you

17:12 < KimSangYeon-DGU> sumedhghaisas: I will try to focus on verifying the equations and thinking about how to implement them harmonizing the existing mlpack's codebase.

17:12 < KimSangYeon-DGU> sumedhghaisas: If I have a question about that, could I ask you?

17:12 < sumedhghaisas> KimSangYeon-DGU: That sounds great. I should warn you, there are some typos in the paper. :)

17:12 < sumedhghaisas> so feel free to ping me if you get stuck

17:14 < KimSangYeon-DGU> Thanks.

17:15 < KimSangYeon-DGU> I'll be careful!! :)

18:03 < rcurtin> lenixlobo: ok, I'd suggest that you take a look at the mlpack codebase and follow the suggestions in http://www.mlpack.org/involved.html and http://www.mlpack.org/gsoc.html

18:20 < KimSangYeon-DGU> rcurtin: Is it okay to close the PR myself?

18:22 < rcurtin> KimSangYeon-DGU: of course, that's just fine :)

18:23 < KimSangYeon-DGU> Aha, Thanks!!

19:12 < jenkins-mlpack2> Yippee, build fixed!

19:12 < jenkins-mlpack2> Project mlpack - git commit test build #117: FIXED in 42 min: http://ci.mlpack.org/job/mlpack%20-%20git%20commit%20test/117/

19:12 < jenkins-mlpack2> noreply: Merge pull request #1742 from saksham189/brr