638 karma · joined August 14, 2012
1. Online dating mostly depends on looks, it's based on your pictures. Almost no woman reads your bio. Simply because people don't have time (this is another reason why new apps like tinder and bumble even don't give you enough spaces to write about yourself, because it doesn't matter at the end).
2. If a woman does not find an attractive man "interesting" after first couple of dates or so, she can easily find another attractive man with "interesting" personality. They do not have to go for an ugly male with interesting personality. Because women (even average/non-attractive ones) have an extremely large dating pool, men don't. For example:
https://forum.bodybuilding.com/showthread.php?t=168948903&pa...
3. Almost no woman picks a mate based on political views. I have seen die hard leftist women dating neonazi guys.
https://mtonews.com/.image/t_share/MTUzODEzNzk4OTYyMDc5NDg2/...
Same sex (specially between men and men) is a completely different ballgame. The mate choice strategy of women is completely different from that of men.
But I guess everyone is completely disregarding the basics of human nature, attraction is mostly based on "looks/facial aesthetics":
https://www.reddit.com/r/BlackPillScience/
In a free/unregulated sexual market, women are the selectors, not men. Women are biologically hardwired to select good looking males (irrespective of his money, personality or social status). Therefore ugly people don't get matches. Very simple.
No matter how many ways you try to engineer/fix the system, it's not going to work. Because the existing system is already doing what it is supposed to do.
https://www.rbth.com/blogs/2014/11/01/sanskrit_and_russian_a...
Please get a copy of Russel-Norvig's A.I. text and read at least some part of it before clicking that link.
https://physics.stackexchange.com/questions/270969/is-it- possible-to-noise-cancel-a-sonic-boom
I don't understand. How do you prove a gradient descent is guaranteed to escape local minima?
1. It's theoretically impossible to guarantee a convergence to global optima using gradient descent if the function is non-convex.
2. The only way to guarantee is to start the gradient descent from different points in the search space or try with different step sizes if the algorithm only starts from the same point in the search space.
3. Also does "achieving zero training loss" mean the network has converged to the global optima? I used to know you will get zero training loss even if you are at a local minima as well.
Please correct me if I am wrong.
https://visual.ly/community/infographic/science/prehistoric-...