Eigenapplication: EXPLOSIONS?

I'm here to ask you one question, and one question only.

How To Make Alt Key Not Type Unicode On Macos

I actually prefer the term macOS to OSX.

Eigenblogpost, or: Eigen Do It If I Try

Prerequisites (In Order Of Importance)

How to Get Rid of the Tildes in Vim 8 and Neovim

If your buffer has empty lines, they are filled with annoying tildes. Vim 8 and Neovim finally fix this by allowing you to conceal them with the following command:

Mnenomic for Law of Total Variance

I have (what I find) a handy mnemonic for the law of total variance. It's also sometimes called the conditional variance formula, or (the reason for this mnenomic) Eve's law. At least if I'm quoting Wikipedia right.

Notation for Stochastic Functions

I forget where I've seen this, but I like using a squiggly arrow to represent a function that returns a probability.

Numpy Tranpose Explained

(n,) vs. (n, 1)

Importance Sampling, Trivialized

Algebraic manipulation is always a fun way to make something profound seem like mere trickery.

I No Longer Like Python's Boolean Comparisons

1 or 2 # returns 1, not True
(1 or 1) == True # returns False

Fixing Annoying Issue With Matplotlib Pyplot In macOS

Running plt.show() causes all sorts of issues on macOS. To fix them, switch your matplotlib backend to something besides MacOSX. I used Qt5Agg.

Some Things Don't Change

From I Want to be a Mathematician, Paul Halmos's autobiography.

Deep Learning Book Chapter 11 Notes

Understanding the fundamentals in chapter 5, especially over and underfitting, really pays off. This chapter is really easy to read if you can even half-remember those earlier sections.

Best Advice I Ever Got About Doing Math

I'm glad I read this early on, because it's kept me going ever since.

Matrix Calculus

This is a topic near to my heart, in the same way that an old bullet stuck in your chest is.

Postmodern Mathematics

Why do we care about true statements?

Why are we privileging truth over falsity? There's nothing inherently better about inconsistent models.

Deep Learning Book Chapter 1 Notes

Started shallow, going deep.