Min-Max Normalization

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

anew =a−min⁡Amax⁡A−min⁡A(max⁡−min⁡)+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.