In the context of machine learning, what is the relationship between "Normalization" and "norm"
Let's take L2 norm as an example.

This figure from wiki illustrates "in a right-angled triangle the square of the hypotenuse is equal [to the sum of] the squares of the two other sides"



given a = 3, b = 4, then c = 5.

consider c as a vector,
$
c =
\begin{bmatrix}
3 \\
4 \\
\end{bmatrix}
$

the L2 norm of c is the length (magnitude) of c = 5.

to normalize the vector c, divide each component by its norm 5, then the normalized vector = 
$
\begin{bmatrix}
3/5 \\
4/5 \\
\end{bmatrix}
$

this processing is called normalization.