Summary of GSoC'21 (under Tensorflow) with pyprobml
M y journey with pyprobml started in march'21 after going through the announced GSoC projects. I worked on making the legacy code of probml in python and also on new code for the freshly-brewed topics from the vol2 (in making!) of the book Almost every figure in the books will be generated by a script/notebook that's optimized and thoroughly documented. This has been amazing with all the new learnings-unlearnings, challenges and accomplishments. In this blog, I would like to share all my work(that's been available publicly) for the pyprobml codebase and also a bunch of awesome! things that I have witnessed and learnt during this period. My contributions to the probml org: Below are the list of PRs that's been merged, each of them refer to a specific column in the books. Pre-GSoC: Cluster yeast data using kmeans 🔗 Different kernel binary classifiers demo on bishop data 🔗 Kernel regression demo using NW Smoother 🔗 Ridg...
