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 [ 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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