Update EUCLIDEAN_DISTANCE.md (microsoft#6625)

Corrected the capitalization of the page header/title. I also removed
the manually typed math and replaced it with inline LaTeX display math
to make the formulas easier to read as well as to add a more
professional look.

For some reason, when using the `$$` LaTeX delimiter, GitHub sometimes
cuts off the vertical portion of large square root symbols. I sort of
see that happening here with the second formula, but when I zoom in and
out, it corrects itself, so I'm unsure if it's how the browser is
drawing the formula, or if the issue originates solely with GitHub. If
it's present on the screen of any reviewers, please let me know and I'll
switch the code to use the single `$` LaTeX delimiter which doesn't have
this problem. The only downfall with the single `$` delimiter is that it
defaults to left alignment instead of center alignment, but I'm pretty
sure that can be manually adjusted.

Take Care

### Motivation and Context

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### Description

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### Contribution Checklist

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- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
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- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄

Author: TechProofreader

docs/EUCLIDEAN_DISTANCE.md

@@ -1,15 +1,15 @@
-# Euclidean distance
+# Euclidean Distance
 
 Euclidean distance is a mathematical concept that measures the straight-line distance
 between two points in a Euclidean space. It is named after the ancient Greek mathematician
 Euclid, who is often referred to as the "father of geometry". The formula for calculating
-Euclidean distance is based on the Pythagorean theorem and can be expressed as:
+Euclidean distance is based on the Pythagorean Theorem and can be expressed as:
 
-    d = √(x2 - x1)² + (y2 - y1)²
+$$d = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}$$
 
-In higher dimensions, this formula can be generalized to:
+For higher dimensions, this formula can be generalized to:
 
-    d = √(x2 - x1)² + (y2 - y1)² + ... + (zn - zn-1)²
+$$d(p, q) = \sqrt{\sum\limits_{i\=1}^{n} (q_i - p_i)^2}$$
 
 Euclidean distance has many applications in computer science and artificial intelligence,
 particularly when working with [embeddings](EMBEDDINGS.md). Embeddings are numerical