Using these values, we can standardize the first value in the dataset of 20.7 as follows: 1 2 3 4 y = (x - mean) / standard_deviation y = (20.7 - 10) / 5 y = (10.7) / 5 y = 2.14 The mean and standard deviation
Normalization is useful when your data has varying scales and the algorithm you are using does not make assumptions about the distribution of your data, such as k-nearest neighbors and artificial neural networks. Standardize Your Numeric Attributes Data standardization is the process of rescaling one ...
Kick-start your project with my new book Deep Learning for Computer Vision, including step-by-step tutorials and the Python source code files for all examples. Let’s get started. How to Normalize, Center, and Standardize Images With the ImageDataGenerator in KerasPhoto by Sag...