Oh, my mighty homework. Thanks to @pyos, dg language is so capable and relaxing. You don't have to worry about most of the stuff.
This POC is intended to guarantee my high score on Machine Learning (UIST602), postgraduate, OMU, Data Science Mast.
Understanding programming can be a huge challenge. Even in this cosmopolitan field, Data Scientists are suffering from having thin (or none) background of Software Engineering or basicly, SE principles, et. al.
Solving a problem with more than one perspective can give person not just another perspective on the problem and set of solutions, it can give a better understanding over things. So, the surface is the same (the problem) but the eye-range is more, imagine that you are looking with a different tool other than your glasses, like microscope, or something like that. Seeing deeper is helpful. Conceptually, this approach is being in use by many education professionals or cult organizations.
You can follow this repository easly. The filenames are matching with original homework implementations of mine. Not just the filenames, since the original homework's specification contains Jupyter Notebook implementation, you can match Input by Input. You can compare two implementations, first Python on Jupyter Notebook and Dg implementation.
Educational purposes only!
Let's clear about what you need to have in order to see the Sun!, (sorry) the graphics of the datasets!
Obviously. But preferably, v3.6.15.
Install and test the dg language by original repository.
In order to run Doge in Jupyter Notebook ecosystem, I wrote a naive (as a starter) Kernel for Jupyter. So, we finally can run Doge as Notebook!
After meet the requirements, you can run the files by Jupyter Notebook.