Thanks for stepping me through that. I was able to reread and understand your original explanation. To be honest, though, I was really just trying to understand what my roadblock is in general. As I mentioned, I…
I wasn’t trying to be critical - sorry if it came across that way! > Also, "a fixed number of features ... call it d_f", I don't know how to more clearly define what d_f is? Perfect example. I would expect a fixed…
> You have a collection of N things, with some fixed number of features each (doesn't matter how many, but call it d_f). Call that collection {x_i}. You have two learnable matrices Q and K, which learn to project those…
Thanks for stepping me through that. I was able to reread and understand your original explanation. To be honest, though, I was really just trying to understand what my roadblock is in general. As I mentioned, I…
I wasn’t trying to be critical - sorry if it came across that way! > Also, "a fixed number of features ... call it d_f", I don't know how to more clearly define what d_f is? Perfect example. I would expect a fixed…
> You have a collection of N things, with some fixed number of features each (doesn't matter how many, but call it d_f). Call that collection {x_i}. You have two learnable matrices Q and K, which learn to project those…