One must question the value of a metaphor that requires more explanation than the thing it is being compared to. That said, I understand vector homomorphisms, eigenvectors, and eigenvalues reasonably well, and yet I am somewhat baffled by the analogy here.
Anyway, "eigencapital" is also a transliteration of the German "Eigenkapital", which, when used of a person, means "your own capital". That also works, I think, although it loses the clever allusions to regression and machine learning.
I like book reactions like this even more than reviews as they are an emotional TL;DR. To many reviews just 'unpack' the book. I know, some people love unboxing videos…
They show actual thinking in progress. Makes blogging a tutorial grove.
I did this with a physics mathematics book (cosmology no eigens) almost three times. Julian Barbour's The Janus Point. And now I have fallen in love with my own metaphor the tensegrid, i.e. tensegrity as a replacement for the Newtonian space-time crystalline grid that Einstein's relativity maths has to 'bend' in order to make it fit the data-was-prediction. With a tensigrity metaphor there is no need to use themetaphor to bend at all.
So keep up the metaphor work, even if they are really analogies:
I have not read the book but it is definitely an interesting eidea (forgive me).
Though food for thought: eigenvectors are in some way the emergent categories of things, where for example were your blog boring and predictable, we would find one Davies vector of a blog, which we can scale (more or less) for the next installment at our leasure.
However, "the system" prefers set its own dimension of measurement (whiteness, wealth, heterosexuality, what have you) which would be rather bleak to turn out to be in some ways naturally eigenvectors.
Which goes some way to explain why the "lossy JPEG" of you the New York Times has feels nothing like you. Choose the wrong vectors, get the bad compression.
One must question the value of a metaphor that requires more explanation than the thing it is being compared to. That said, I understand vector homomorphisms, eigenvectors, and eigenvalues reasonably well, and yet I am somewhat baffled by the analogy here.
Anyway, "eigencapital" is also a transliteration of the German "Eigenkapital", which, when used of a person, means "your own capital". That also works, I think, although it loses the clever allusions to regression and machine learning.
Joke: suddenly Stirner memes start appearing
I like book reactions like this even more than reviews as they are an emotional TL;DR. To many reviews just 'unpack' the book. I know, some people love unboxing videos…
They show actual thinking in progress. Makes blogging a tutorial grove.
I did this with a physics mathematics book (cosmology no eigens) almost three times. Julian Barbour's The Janus Point. And now I have fallen in love with my own metaphor the tensegrid, i.e. tensegrity as a replacement for the Newtonian space-time crystalline grid that Einstein's relativity maths has to 'bend' in order to make it fit the data-was-prediction. With a tensigrity metaphor there is no need to use themetaphor to bend at all.
So keep up the metaphor work, even if they are really analogies:
https://whyweshould.loofs-samorzewski.com/reaction-review-of-the-janus-point.html
https://whyweshould.loofs-samorzewski.com/is-the-universe-a-calculator.html
https://whyweshould.loofs-samorzewski.com/minimum-viable-product.html
I will steal the
Joke:
format
You *hope* there's nobody else with your ID number. If the other guy turned out to be a sociologist, it could explain a lot.
I have not read the book but it is definitely an interesting eidea (forgive me).
Though food for thought: eigenvectors are in some way the emergent categories of things, where for example were your blog boring and predictable, we would find one Davies vector of a blog, which we can scale (more or less) for the next installment at our leasure.
However, "the system" prefers set its own dimension of measurement (whiteness, wealth, heterosexuality, what have you) which would be rather bleak to turn out to be in some ways naturally eigenvectors.
Which goes some way to explain why the "lossy JPEG" of you the New York Times has feels nothing like you. Choose the wrong vectors, get the bad compression.