lol

SVC's founder is gently crying in the background

It also said something like support vector methods being better for some problems. But they seem to be right for most of the problems I've used tpot on hah

Do you use existing architectures and just modify parameters for your work or do you try to come up with new ones?

well as far as classifications go...there aren't too many other popular methods...

All the boosting algorithms are generally done on trees

Neural nets + support vectors

what else is there...

Naive Bayes

mmm kNN

depends on the project

sometimes I come up with my own architectures :D

N-sided die roll!

ah yeah I guess there's nearest neighbor methods...

And that sounds fun

I built a deep residual variational autoencoder

:D

not working as well as I'd hoped so far though lol

I want to try my own architectures at some point, but I suppose I should actually figure out how the standard architectures work first

Are autoencoder networks used a lot?

uhm...not sure about their popularity

I think right now GANs are more popular

(they don't quite do the same thing, but generally speaking it seems GANs has more interest)

Do Ng's courses go into them?

GAN's? Not that I recall.

At least not in detail.

But his sequence models has Attention-based models which is pretty cool