I need a mathematically simplistic, multidimensional dataset that can be generated with machine accuracy.
I’ve been using [x,y] with truth determined by some hidden function, but it’s hard to generalize that beyond 3 dimensions. It does have the advantage of being accessible to machine limits, though.
But what if…more dakka isn’t enough dakka?
Then…even more dakka!
On a related note, I’m running models with hundreds of thousands to millions of neurons a layer.
Sometimes I just start doing a little PCA and lose all sense of time.
It happens more than you’d think, and while that’s probably a low bar to get over, it’s kinda weird that it crops up as often as it does.
I’m trying to get a good picture of Danny Devito as Frodo Baggins but haven’t succeeded yet.
Stable Diffusion does landscapes pretty well.
I’d like to use it to creat human models I can practice drawing, but the details are what SD gets wrong and what I need to get right.
I haven’t gotten any good shots of Castle Amber, FYI.
You want your features to induce four things: information, linear independence, resilience, and speed.
Example: you want to figure out who someone is.
Obviously, your features should have enough information that the model can accurately answer a question. A good feature is a name.
You don’t want redundant features. The first character of the name is highly linearly dependent with the name. Not perfectly, as you could get noise on the line, a misclick, or something similar, but very, very dependent. The first character of the name would be less useful if the name is another feature.
You don’t want a system so light that noise in any one feature throws the model off. So you don’t only want the name as a feature because people change their names, use nicknames, and so forth. If all you’ve got is the name, and Thomas is going by Tom, you’ve got a problem. A problem that the first letter of the name as a name might solve.
Computers don’t know the connection between Thomas and Tom, much less Elizabeth and Liz. So you’ve got to make judgement calls. But a balance of the three aspects such that the programmer maximizes the norm is a good direction to start.
Of course, you also want your model to work on finite hardware, so speed maximization is a factor. Maximize norm(I, LI, R)*speed.