From b87be7808d7aeb0d499f70318139eaf5e44020b0 Mon Sep 17 00:00:00 2001
From: ben-jaynes <1btjaynes@gmail.com>
Date: Sun, 24 May 2026 19:14:01 -0700
Subject: [PATCH] vault backup: 2026-05-24 19:14:00

---
 .obsidian/hotkeys.json                         | 17 +++++++++++++++++
 Wiki/Linux/Full Fedora Install Instructions.md |  6 +++---
 Wiki/Machine Learning/Classification Models.md |  2 +-
 3 files changed, 21 insertions(+), 4 deletions(-)
 create mode 100644 .obsidian/hotkeys.json

diff --git a/.obsidian/hotkeys.json b/.obsidian/hotkeys.json
new file mode 100644
index 0000000..70919da
--- /dev/null
+++ b/.obsidian/hotkeys.json
@@ -0,0 +1,17 @@
+{
+  "command-palette:open": [
+    {
+      "modifiers": [
+        "Mod"
+      ],
+      "key": "P"
+    },
+    {
+      "modifiers": [
+        "Mod",
+        "Shift"
+      ],
+      "key": "P"
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Wiki/Linux/Full Fedora Install Instructions.md b/Wiki/Linux/Full Fedora Install Instructions.md
index ca5a7db..5aa9c1b 100644
--- a/Wiki/Linux/Full Fedora Install Instructions.md	
+++ b/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
diff --git a/Wiki/Machine Learning/Classification Models.md b/Wiki/Machine Learning/Classification Models.md
index 53afca9..89a0766 100644
--- a/Wiki/Machine Learning/Classification Models.md	
+++ b/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.
\ No newline at end of file
+$$\frac{(\mu_{1}-\mu_{2})^2}{s_{1}^2-s_{2}^2}$$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.
\ No newline at end of file