Scaling of data in neural network models

Data scaling or normalization is a process of making model data in a standard format so that the training is improved, accurate, and faster. The method of scaling data in neural networks is similar to data normalization in any machine learning problem.

Some simple methods of data normalization are listed here:

• Z-score normalization: As anticipated in previous sections, the arithmetic mean and standard deviation of the given data are calculated first. The standardized score or Z-score is then calculated as follows:

 

Here, X is the value of the data element, μ is the mean, and σ is the standard deviation. The Z-score or standard score indicates how many standard deviations the data element is from the mean. Since mean and standard deviation are sensitive to outliers, this standardization is sensitive to outliers.

• Min-max normalization: This calculates the following for each data element:

Here, x_i is the data element, min(x) is the minimum of all data values...