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 <[email protected]> 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 <[email protected]> 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 > > [email protected] > > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack > >
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