Min-Max Normalization

Scales values to some interval $[max,min]$:

$a_{\text{new }}=\frac{a-{\min }_A}{{\max }_A-{\min }_A}(\max -\min )+\min$

This has the advantage that all values lie in a defined interval which might be important for certain tasks. For example Deep Learning Models need the data to be between 0 and 1.