Hi Avtansh,

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
To: mlpack@lists.mlpack.org
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.


Avtansh Tiwari


Sent from Mail.Ru app for Android
mlpack mailing list

Reply via email to