But while I was building my own artificial neural networks, I needed to transform the normalized output back to the original data to get good readable output for the graph.

In the business world, "normalization" typically means that the range of values are "normalized to be from 0.0 to 1.0". "Standardization" typically means that the range of values are …

I'm doing principal component analysis on my dataset and my professor told me that I should normalize the data before doing the analysis. Why? What would happen If I did PCA without …

After you normalized, your data ranged from $0$ to $1$. That is, a change of one unit now means going from the lowest valued observation to the highest valued observation.

I have seen in some algorithm, people uses normalized gradient instead of gradient. I wanted to know what is the difference in using normalized gradient and simply gradient.

What im thinking is lets say number 200 to be normalized so it falls between a range lets say 0 to 0.66 or 0.66 to 1 or 1 to 1.66. The range being variable as well.

one thing i've found is how you will compare these two eventually, and how the normalized time series will affect the behavior of that comparison method. Let's say I have a set of original …

This is frequently why features are one-hot encoded. By normalizing all features to a 0-1 range, it prevents certain features from having strong importance than others. Conversely, if you some …

Feb 4, 2015 · The normalized squared euclidean distance gives the squared distance between two vectors where there lengths have been scaled to have unit norm. This is helpful when the …

In Wikipedia the cross-correlation in time series analysis is defined exactly like you wanted, i.e. mean centered and normalized. You're referring to the definition in signal processing.