# 4 must-know normalization techniques for data scientists 4 must-know normalization techniques for data scientists
If you have been practicing machine learning and deep learning for some time, you should have come across this word normalization.
What is this normalization or standardization and how this technique is helping data scientists and AI engineers all over the world to help improve their models?
In this article, we will be discussing four types of normalization techniques that are quite popular among the community.
1. Standardization
4. Local Response Normalization
STANDARDIZATION
Standardization is nothing but converting your data into a standard format. What is this standard format you may ask? A standard format is that format of data when the mean of the data is “Zero” and the standard deviation of that data is “One”.
It means the data will range from -1 to 1, also one more important point is that the distribution of the standardized data will look like a Gaussian curve or a bell curve.
So, how can we convert our data into standardized data?
The answer lies in the definition itself. We have to first find the mean and standard deviation of the data. subtract every point of the data with the mean we just found and then divide it by the standard deviation of the whole data. (It has been seen that the fractional power of any data-set also behaves like a normal distribution like x⁰⁴, x⁰², etc(here 04 means 0.4 and so on))
Image is taken from https://media.vlpt.us/images/jiselectric/post/8862ef9a-13a2-4402-8c80-1929d7c37083/0_PXGPVYIxyI_IEHP7.png
NORMALIZATION
Normalization is much simpler than standardization. it is just re-scaling of our data into a particular range. Generally, that range is from 0 to 1 but you can take any range.
A very famous way to normalize your data is min-max normalization. all we have to do is to find the minimum and maximum value from our data.
subtract every point of the data with the minimum value and divide it by the difference of the minimum and maximum value.