Tutorial 93 - Deep Learning terminology explained - SCALING
Scaling is important in machine learning as most models are sensitive to magnitude of features.
e.g., Age (0-100) and House prices (100K to few Million)
Scaling features (attributes) in machine learning is one of the most critical steps of pre-processing data. Scaling Refers to bringing all values to a uniform scale (e.g., between 0 and 1). The term ‘normalization’ is used when we want to bound our values between two numbers, typically, between [0,1] or [-1,1].
This tutorial explains the need for scaling and demonstrates various scaling mechanisms using python.
Code associated with these tutorials can be downloaded from here: https://github.com/bnsreenu/python_fo...