In Chapter 2, Learning Process in Neural Networks, we scaled the data before building the network. On that occasion, we pointed out that it is good practice to normalize the data before training a neural network. With normalization, data units are eliminated, allowing you to easily compare data from different locations.
It is not always necessary to normalize numeric data. However, it has been shown that when numeric values are normalized, neural network formation is often more efficient and leads to better prediction. In fact, if numeric data are not normalized and the sizes of two predictors are very distant, a change in the value of a neural network weight has much more relative influence on higher value.
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