This looks like the kind of class more people need to see. It's less "how to implement" things and more why things do or don't work.
I find this dreadfully important because when you study this math you realize that more often than anyone expects, standard ML is extraordinarily fragile, but also has some powerful justification. For instance, this[1] made me laugh with joy.
I agree that understanding the theory is very important.
The figure is very interesting. Would you care to explain it? I think I know what these are in theory, but perhaps I haven't internalized them enough to understand the visual representation.
Comments
This looks like the kind of class more people need to see. It's less "how to implement" things and more why things do or don't work.
I find this dreadfully important because when you study this math you realize that more often than anyone expects, standard ML is extraordinarily fragile, but also has some powerful justification. For instance, this[1] made me laugh with joy.
[1] http://work.caltech.edu/images1/canvas.png
> this[1] made me laugh with joy.
Whereas it went completely over my head...
I agree that understanding the theory is very important.
The figure is very interesting. Would you care to explain it? I think I know what these are in theory, but perhaps I haven't internalized them enough to understand the visual representation.