fushuan

Maybe that's why bro brought girls.

I don't want to generalise but... Yeah... That's a big reason why a lot of manhuas just suck.

Thanks for the recommendation! I liked Feng Shen Ji too to be honest. It's one of my favorites haha.

It's just... So deserved, you know? Sometimes you can't but laugh in the face of such karma and fucking irony.

Not cuda, but a lower level nvidia proprietary API, your point still stands though.

They replaced it with a lower level nvidia exclusive proprietary API though.

People are really misunderstanding what has happened.

That's the problem, I love wuxia, I love stupid power levels and all that, I love my trash comics. It's the story they tell alongside that that irks me to no end.

Close to zero decent dialogue between people besides shitty villains talking big until the hero flips his eyelash and suddenly everyone is bowing to them. There's no interesting characters, no banter.

To give some examples of wuxia done right (I like the top batch more but the other ones are alright):

Return of the mount hua sect Infinite levelling: murim Return of the crazy demon Nano machine Absolute sword sense Star instructor or something

I reincarnated as the crazed heir Path of the shaman

Half wuxia: Murim login

All of these have a mc with personality, there's decent banter between characters, there's an interesting plot, plot changes make sense...

There's also the "dungeon" theme with portals that grew in popularity since solo levelling (around then) that is basically the same trash plot with another power level vector.

It do be like that with manga/manhua tbh. I still prefer Korean webcomics because I like my trash but... It's been ages since I read a decent Chinese webcomic or manhua. They are so fucking trash. I'm sure there are some good ones but... Yeah.

Not enough for it to make results diverge. Randomness is added to avoid falling into local maximas in optimization. You should still end in the same global maxima. Models usualy run until their optimization converges.

As stated, if the randomness is big enough that multiple reruns end up with different weights aka optimized for different maximas, the randomization is trash. Anything worth their salt won't have randomization big enough.

So, going back to my initial point, we need the training data to validate the weights. There are ways to check the performance of a model (quite literally, the same algorithm that is used to evaluate weights in training is them used to evaluate the trained weights post training) the performance should be identical up to a very small rounding error if a rerun with the same data and parameters is used.

Holy shit thanks I wasn't getting it.

Hey, I have trained several models in pytorch, darknet, tensorflow.

With the same dataset and the same training parameters, the same final iteration of training actually does return the same weights. There's no randomness unless they specifically add random layers and that's not really a good idea with RNNs it wasn't when I was working with them at least. In any case, weights should converge into a very similar point even if randomness is introduced or else the RNN is pretty much worthless.

Normal glasses here are 0.25 litters more or less. That's around 4 glasses a litter.

37 glasses are 9.25 liters. Rounded down to 9 because people don't fill their classes to the brim, idk.

Your numbers scare me.