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We already know that non-real-valued fields of a data set to be modeled by neural network or some other machine learning approach must be transformed into real value. Some categorical or nominal type field may be one-hot-encoded and some natural language token may be embedded into vector.

What about for already real-valued data field? If I want to build a regression model using

http://lib.stat.cmu.edu/datasets/boston

I don't have to normalize the data set further because they are all real-valued? The reason for posting this is that when there is a big difference in magnitude of each dimension, it feels like I have to re-scale them to have the same min, max value, for example, 0.0~1.0.

Gist of my question : There is no need for transformation for already real-valued data field to map them into vector space?

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