Neural Networks with R

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. ...