mindesc

If you have any system that tries to gravitate to a local minimum it is almost impossible to not make Newton's fractal with it. Classical feed forward network learning does pretty much look like newtons method to me. Please take a look into https://en.m.wikipedia.org/wiki/Newton%27s_method

This is how you turn unwanted dependencies and inability to make string searches a virtue.

yeah. use raw datatype url provided by the language and get hacked by some exotic xss you are not aware, because the specs have kitchen and sink included

I have understood that the bronze age collapse was a climate fluke that resulted into famine, which did lead to military power balance change which did lead into trade route collapse.

Dancing links has been so far my favourite algorithm to learn

if entanglement is part of information compression happening on the human brains, that would be wild indeed

The mistake here could also be that they are building a snake and that the real algorithm is about minimal ring containing start and end node. Usually when solving a puzzle the bad decision is taken as the first step and it is some sort of ego barrier that is hard to get over with

starting everywhere and building bigger snake from smaller ones sounds like quantum version of classical inside outside algorithm that is used to check if an input follows certain probabilistic context free grammar. if you don't know it, might be hard to find it https://en.wikipedia.org/wiki/Inside%E2%80%93outside_algorit...

Filling up that mana bar is not easy.

It could be also a sign of dependency hoarding and making you the bottleneck of the whole project. Bad architectural decisions, narcissistic need of importance or both. With those hours your partner starts to date with your friend. With experience I can assure you that position is not worth it. Not for you and not for the project. You end up draining your imagination. Over fitting is emerging in programming like it is emerging in the machine learning.

Those are used. Search for minimum description principle and entropy based classifier. The performance is poor, but it is definitely there and really easy to deploy. I have seen gzip being used for plagiarism detection as similar text tends to compress better. Use the compression ratio as weights on spring model then for visualisation. Also works with network communication metadata ...

Gzip as a classifier is surprisingly good and you should use it as a benchmark for your neural network.

might do good for you to google the minimum length principle (MDL). minimum length classifier has been used for image classifying, so not a new invention. our research group used minimum description length for generating new test material. the archive test suite that found several cvs from the gzip itself was done like so also.