vault backup: 2026-05-24 19:14:00

Wiki/Linux/Full Fedora Install Instructions.md

@@ -50,6 +50,9 @@
 		19. SwayNotificationCenter
 			1. https://github.com/ErikReider/SwayNotificationCenter
 		20. flameshot
+		21. libreoffice
+		22. vlc
+		23. picard
 	2.  [nirimod](https://github.com/srinivasr/nirimod#installation)
 		1. cairo-devel (dnf)
 		2. python3-devel (dnf)
@@ -85,11 +88,8 @@ Still want to install:
 - gimp
 - jellyfin
 - some sort of image viewer/basic preview software (like on mac)
-- vlc
-- libreoffice/openoffice
 - localsend
 - makemkv
-- picard
 - orcaslicer
 - parabolic
 - planify

Wiki/Machine Learning/Classification Models.md

@@ -63,4 +63,4 @@ The algorithm is referred to as "naive" because it makes a few assumptions:
 Linear Discriminant Analysis (LDA) is a supervised learning method that is used to reduce the dimensionality of a dataset. It attempts to maximize the distance between groups and minimize the variation within classes. Since it is supervised it knows the category labels and uses them to maximize the distance between classes. 
 ![[LDA.excalidraw]]
 One way to think about this is that you are attempting to maximize
-$$\frac{(\mu_{1}-\mu_{2})^2}{s_{1}^2-s_{2}^2}$$where $\mu$ is the average of the data and $s$ is the spread. The top of the equation represents the distance between the average of the data projected on the new line and the bottom attempts to minimize the scatter within each category.
+$$\frac{(\mu_{1}-\mu_{2})^2}{s_{1}^2-s_{2}^2}$$where $\mu$ is the average of the data and $s$ is the spread. The top of the equation represents the distance between the average of the data projected on the newline and the bottom attempts to minimize the scatter within each category.