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DOC: Update tutorial-svd.md fix a typo #208

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DOC: Update tutorial-svd.md fix a typo #208

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Expand Up @@ -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

$$Y = 0.2126 R + 0.7152 G + 0.0722 B$$
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