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. Thanks, Adeel ________________________________ From: mlpack <mlpack-boun...@lists.mlpack.org> on behalf of avta...@iitk.ac.in <avta...@iitk.ac.in> Sent: Friday, March 2, 2018 2:57 PM To: mlpack@lists.mlpack.org Subject: [mlpack] Regarding contributing to MLpack Sir/Mam, 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. Regards, Avtansh Tiwari avta...@iitk.ac.in -- Sent from Mail.Ru app for Android
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