On Wed, Feb 22, 2017 at 05:07:39PM +0500, Kirill Mishchenko wrote: > Hi, > > my name is Kirill. I’m interested in the contribution to the project > “Cross-validation and hyper-parameter tuning infrastructure”. I have > already gone through some starting steps, like building the code and > running a few ML algorithms (more precisely, I have did it for Linear > Regression and Logistic Regression). Now I’m going to read rigorously > the wiki page "Design Guidelines” and to go through the interfaces in > the code base . Are there any other suggestions how I can start to > work on the project? Is there some way to make a related small > contribution to the code base?
Hi Kirill, The cross-validation and hyper-parameter tuning project is pretty new, and there is not much in the way of existing bugs that will help understand it since the project involves generating a completely new piece of code for mlpack. I just opened some issues for the decision tree code today; maybe you can find one of those interesting? https://github.com/mlpack/mlpack/issues (the top 5 are related to decision trees, at least when I wrote this email) I think one approach would be to use the various different classifiers and functionality inside of mlpack, and then write some simple C++ programs to do cross-validation or hyper-parameter tuning by hand. Then, this could help make it more clear what the needs of the hyper-parameter tuning module and cross-validation module would be. Maybe these pages are also helpful: http://www.mlpack.org/involved.html http://www.mlpack.org/gsoc.html There are also other issues open in the Github issue tracker, and any contributions of new techniques or efficiency improvements for existing implementations are always welcome. > Briefly about myself. I am a PhD student working on Computational > Humor. More precisely I’m working on the problem of finding/generating > a humorous response given a textual input. My programming experience > includes two summer internships in big Russian IT companies: in one I > was programming in C# (SKB Kontur), in another I was a C++ developer > (Yandex search). In daily life I use Python. I have taken the online > course Machine Learning by Stanford (Coursera), as well as some other > courses related to ML (AI by Berkeley (EdX), Deep Learning by Google > (Udacity), and others). Wow, computational humor, that is very cool! There was a group that I worked with briefly at Georgia Tech on computational humor: http://www.vip.gatech.edu/teams/humor-genome I gave a talk to that group on the mlpack collaborative filtering code, and I think that one point they were using mlpack_cf as a recommender system for jokes, but I am not sure what came of it. I will have to ask... I always thought it would be interesting to use generative deep neural networks to try and generate jokes. I don't think they would be good jokes, but I think they would be funny for the same reason my favorite comic Garkov is funny: http://joshmillard.com/garkov/ I'd be interested to hear more about what you are doing there, if you'd like to elaborate. I think that is a very neat field. Thanks, Ryan -- Ryan Curtin | "If it's something that can be stopped, then just try to stop it!" [email protected] | - Skull Kid _______________________________________________ mlpack mailing list [email protected] http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
