Feature Normalization

0 - 1 Normalization

xj[i]=xj[i]−min(xj)max(xj)−min(xj)x_j^{[i]} = \frac{x_j^{[i]} - min(x_j) }{max(x_j) - min(x_j)}

Z-score Normalization

More recommended when using DL methods (due to the zero-centering)

xj[i]=xj[i]−mean(xj)std(xj)x_j^{[i]} = \frac{x_j^{[i]} - mean(x_j) }{std(x_j)}
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