Merge pull request #208 from partev/patch-1

DOC: Update tutorial-svd.md fix a typo

Author: mattip

content/tutorial-svd.md

@@ -154,7 +154,7 @@ $$U \Sigma V^T = A$$
 
 where $U$ and $V^T$ are square and $\Sigma$ is the same size as $A$. $\Sigma$ is a diagonal matrix and contains the [singular values](https://en.wikipedia.org/wiki/Singular_value) of $A$, organized from largest to smallest. These values are always non-negative and can be used as an indicator of the "importance" of some features represented by the matrix $A$.
 
-Let's see how this works in practice with just one matrix first. Note that according to [colorimetry](https://en.wikipedia.org/wiki/Grayscale#Colorimetric_(perceptual_luminance-reserving)_conversion_to_grayscale),
+Let's see how this works in practice with just one matrix first. Note that according to [colorimetry](https://en.wikipedia.org/wiki/Grayscale#Colorimetric_(perceptual_luminance-preserving)_conversion_to_grayscale),
 it is possible to obtain a fairly reasonable grayscale version of our color image if we apply the formula
 
 $$Y = 0.2126 R + 0.7152 G + 0.0722 B$$