Today I grasped the difference between the differential and the derivative (only took 6 years -_-), lost my fear of \(dx\) as a single symbol (until learning the exterior derivative changes that into 2 symbols again), and finally got a hint as to why transposes pop up in weird places in “matrix calculus”.

(This is not meant to show you how to solve the problem, just what the solution does in the most important special case).

TLDR:

No need to build OpenCV from scratch, a task almost as hard as getting the projector dongles to work when you plug them in.

(I sat on this post for a while, but rather than let it rot in my drafts, I decided to publish it even though it’s lacking something.)

This is for anyone trying to be more than just good.

Spent another fun afternoon on the BART.

What I learned today:

This book won’t be a substitute for mindful reflection.

In math, you notice a lot of concepts have multiple definitions, which are formally equivalent but give insight into different aspects of that concept.

Turns out American Dad already did the joke.

I’ve been thinking about the paper Understanding Deep Learning Requires Rethinking Generalization.

Fiction gives concrete examples of abstract lessons, and your brain doesn’t care if it’s made up.¹

This book is about making better decisions. Unlike most books with that theme, it emphasizes thinking rationally.

See SMBC and Nick Bostrom for a longer version of my views.

Use port forwarding.