gams

R is still unbeatable for wrangling tabular data, visualising, and fitting complex models on tabular data (complex up to GAMs let’s say).

For exploring data, plotting data, and fitting complex models like GLMs and GAMs, using R is essentially as fast as thought at this point.

The applied stuff tends to use netlib, gams, gsl, colt, apache math...mostly a lot of gsl these days...again no CT there.

You'd run into all sorts of problems because the Wine implementation differed from the Windows native D3D, and that was enough to break many gams.

You should look into using generalized additive models (GAMs).

Probably the best use of those gams in my day is to look back through things I've already written down earlier in the day.

Unsupervised learning is a large umbrella, under which are algorithms like transformers, RNNs, GAMs, diffusers, etc.

Neural GAMs with learned basis functions have already been proposed, so I'm a bit surprised that the prior art is not mentioned in this new paper.

Conversely, my hockey teammate used to get his later in the afternoon because he was religious about stopping on the way home from our gams which was usually pretty late at night.

Unfortunately mixed up in some sort of strawman - you are angry that people would like to see strong female characters in action games, so you are accusing the parent of only wanting action gams for kids - which is not evident in that post - and therefore hurting the chance for girls (who apparently can't like action games) to get good games for them.

It depends on the "severity" of the violation of assumptions--you can also use GAMs to add flexible nonlinear relationships--and the amount of data you are working with.

You might also enjoy reading the "Neural Additive Model" paper from Hinton's lab, which is basically GAMs using a separate DNN as a "spline basis" for each input variable.