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The opaque nature of all the machine learning today has made me appreciate analytic algorithms more. For a number of years I've been exploring 2D representation
by optrigonian 4y ago
The opaque nature of all the machine learning today has made me appreciate analytic algorithms more. For a number of years I've been exploring 2D representation using triangulations [1]. Working with analytic algorithms is like playing around with synthesizers whereas ML is more like using a sampler.
[1] https://github.com/mpihlstrom/femton https://github.com/mpihlstrom/femton
- xtrgz 4y agoDo you have plans for a license? Impressive either way.
- optrigonian 4y agoThanks. A license has been added now.
- mxmlnkn 4y agoI love the ReadMe of your project. It is like the other extreme when compared with Geogram. I tried to find out what Geogram was about and what it does better but I lost interest before I could find out. The ReadMe needs some work and the wiki didn't seem much better at first glance. You already have to know what you are looking before using Geogram.
- optrigonian 4y agoThanks. I have a low attention span with GitHub repos, so I felt I should at least try to meet the expectations of someone like myself.
- BrunoLevy01 4y agoOoohh yes, I need to do some work on Geogram's readme (targeted towards a super-specific audience of folks who know already what they are looking for there, you are perfectly right). For now, if you want an overview of that Geogram does, the following link gives a description of several sets of functionalities: https://github.com/BrunoLevy/geogram/wiki#mini-tutorials-and-example-programs https://github.com/BrunoLevy/geogram/wiki#mini-tutorials-and...
- Blackthorn 4y ago> Working with analytic algorithms is like playing around with synthesizers whereas ML is more like using a sampler. Amazing analogy that really helps me understand these algorithms better. Thanks for that.