Hello Roshan, Awsome report; it's really nice to see what was added over the summer; looking forward to merge the remaining PR soon, so we can check that off from the list as well. Thanks again for all the contributions.
Thanks, Marcus > On Aug 20, 2021, at 10:29 AM, Roshan Swain <[email protected]> wrote: > > Hi, > I created a work summary for GSoC'21. There were a lot of blockers like long > training time, jupyter kernel crashes(always behaves strangely), and other > personal challenges, but still somehow made it till now. I have compiled my > final works below: > > 1. Implementation of Linear Regression for California Housing: > This includes CPP and Python Notebooks with cool visualizations and > evaluation metrics. > > 2. Implementation of Generative Adversarial Networks for MNIST: > This covers usage of GAN class with documentation and comments on Generator > and Discriminator Architecture. This is a major headache coz it is taking a > lot of time to train and thus very time consuming to optimize. > > 3. Implementation of GAN for Anime Faces Generation: > The idea was to showcase usage of GAN for creation of detailed anime > faces(RGB) but considering the training time, we altered it a bit. We are > gonna translate Pytorch model weights and convert them into mlpack weights. > This is in progress and hopefully should be completed by this week. > > Link to GSoC work report: > https://github.com/swaingotnochill/gsoc-work-report > <https://github.com/swaingotnochill/gsoc-work-report> > [ I will be making updates to the report too accordingly]. > > On a final note, I am very grateful to the mlpack community, especially Ryan > and Marcus for helping me get started with mlpack initially and my mentors > Marcus and Kartik, they always helped me whenever I was in a pinch(I loved > the zoom meetings :D). Also, thanks to David for helping out a lot in this > time period. Throughout this period, I enjoyed contributing to mlpack and > will continue to do so. > Thank You. > > Regards, > Roshan Swain
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