[mlpack] GSOC-2020 | Project Proposal | Application Based Machine Learning Course using MLPack

[Shivam Behl]

Hi Marcus,

I understand your concerns. Thank you for your reply.
The Red Hen Lab project "Design and Develop an online deep learning course
for Humanists" can be found at
https://summerofcode.withgoogle.com/archive/2019/projects/5913691646590976/
 .

Contributing to the organization would be fun, maybe I will keep looking
for Ideas to contribute.

One more question, do you think I should still propose this one?

Thanks,
Shivam

On Sat, Mar 21, 2020 at 4:01 AM Marcus Edel <marcus.edel at fu-berlin.de>
wrote:

> Hello Shivam,
>
> thanks for reaching out, I generally like the idea, however, I'm not sure
> it's
> something we can mentor during GSoC, Google generally expects students to
> code,
> there was Season of Docs last year, which might fit better. You said
> RedHen Labs
> did a similar project, do you have a link to the project?
>
> Thanks,
> Marcus
>
> > On 19. Mar 2020, at 16:58, Shivam Behl <shivambehl123 at gmail.com> wrote:
> >
> > Hello Mentors,
> >
> > I am Shivam Behl, 3rd year undergraduate at Thapar University, India.
> > I want to propose an "application-based Machine Learning course for
> MLPack" for GSOC 2020. I have seen a previous GSOC Project which dealt with
> making machine learning course for Humanity students under the organization
> - RedHen Labs. Please review this short proposal and tell if it is GSOC
> level.
> >
> > I have spent last week learning and understanding MLPack library. During
> this period I realized that while learning MLPack, one is quite on your its
> for a lot of time. Documentation is good, but many parts are not clearly
> explained and one has to refer the source code to understand a lot of
> stuff. This is is not a big deal for a seasoned programmer fluent in C++,
> but can be a cause of trouble for new entrants.
> >
> > I believe having a machine learning course based on MLPack can help
> boost the user base of the library and help find applications in new niches.
> >
> > I propose to make the course around these points -
> > 1. Basic Machine Learning Implementations using C++ and Python Wrapper.
> > 2. Practical applications using Mini Projects.
> > 3. Understanding CodeBase
> >
> > Work will be in three phases, as follows -
> > -- Phase 1 --
> > The intuition of Machine Learning Algorithm.
> > Application using ML Pack.
> > This pattern will be followed for all the Algorithms implemented in
> MLPack.
> >
> > -- Phase 2 --
> > Hands-on Tutorials on Implementing MLPack on free to use Datasets.
> > Interesting visualizations and comparisons with other libraries.
> >
> > -- Phase 3 --
> > Silent C++ features used in MLPack codebase.
> > Basics of how the code is implemented.
> > Appendix of Armadillo, Numpy Stack, etc.
> >
> > The Course will involve slides, pdf for detail text and code snippets.
> >
> > Presently, I am thinking of the following hands-on Tutorials to be a
> part of the course -
> > --- Stock Market analysis and building algo-trading model using basic ML
> Algorithms.
> > --- Style transfer using ANN
> > will add more soon.
> >
> > Please let me know if this project is suitable for GSOC and what changes
> I should make to the overall plan.
> >
> > Regards,
> > Shivam Behl
> > _______________________________________________
> > mlpack mailing list
> > mlpack at lists.mlpack.org
> > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
>
>
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