Thanks for getting in touch.
To get familiar with the mlpack community, please go through the "contributing
to mlpack" guide (http://www.mlpack.org/involved.html). This will help you get
familiar with the design guidelines. Instructions on how to compile mlpack from
source can also be found there. But, if you just want to test out some of the
tools that mlpack offers, you can simply install it using the package manager.
On Ubuntu, this can be achieved with the following command:
$ sudo apt-get install libmlpack-dev
Once you are somewhat familiar with the codebase (going through the tests will
help you understand the program structure), you can move onto solving a bug
from the issues list (https://github.com/mlpack/mlpack/issues). For the
"Essential Deep Learning Modules" project, the papers listed on the project
ideas page might be helpful, but the mentors will be able to give you a more
valuable insight on that.
Let me know if you have any questions.
From: mlpack <mlpack-boun...@lists.mlpack.org> on behalf of avta...@iitk.ac.in
Sent: Friday, March 2, 2018 2:57 PM
Subject: [mlpack] Regarding contributing to MLpack
My name is Avtansh Tiwari and I am a EE Sophomore at Indian Indian Institute of
Technology, Kanpur. I read about mlpack GSoc2018 page and found it interesting.
I am interested in working on Essential Deep Learning Models. I have a working
knowledge of Neural Networks and am currently working on a project that uses
the MXnet Gluon framework for various deep learning algorithms . I am curious
about what I would have to learn and how to contribute to mlpack.
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