Normalization

z-score normalization: This is a way to make all features in the dataset have the same unit (the σ\sigma ). Z=X−μσ Z = \frac{X - \mu}{\sigma} steps are: get mean of feature from all examples ( μ\mu ) get deviation from mean for each item square values from (2) and sum them all Take average of (3), i.e. (3) / m where m is no. of examples Get (4)\sqrt{(4)} ( σ\sigma ) When you do this for each sample feature in a feature set, the range will hence be from +b σ\sigma (gotten from the z norm for number highest from mean) to -b σ\sigma (gotten from the z norm for number lowest from mean) with 0 in the middle (i.e. the mean), where b is a scalar. so cool to understand why they are called “scalar”, because they literally scale what they multiply. ...