Normalized:
1) The data decimal between becomes (0,1) or (1,1). Primarily for convenience of data processing proposed by, the data is mapped to the range of 0 to 1 process, more convenient and fast.
2) The dimensionless expression becomes dimensionless expression, or the order of different units to facilitate the indicator can be compared and weighted. Normalization is a way to simplify the calculations, is about to have a dimension of expression, through transformation into a dimensionless expression become a scalar.
standardization:
standardization:
In machine learning, we may have to deal with different types of data, such as audio and image pixel values, such information may be high dimensions, the data standardization will each feature value of the average becomes 0 (each subtracting the value of average features are characteristic of the original data), the standard deviation becomes 1, this method is widely used in many machine learning algorithms (for example: support vector machines, logistic regression and neural network).
Summary: Data is normalized (row), is transformed; is normalized feature (columns), converts.