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In this first part, we'd like to tell you about some practical tricks for making **gradient descent** work well, in particular, we're going to delve into feature scaling. As an introductory view, it seems reasonable to try to depict an intuition of the concept of *scale*. 


## **Macro, Meso, Micro-scale in Science**

As scientists, we are well aware of the effects of using a specific measurement tool in order to characterize some quantity and describe reality. As an ideal example we consider the **length scale**. 

<img src="https://raw.githubusercontent.com/MLJCUnito/ProjectX2020/master/HowToTackleAMLCompetition/img/Lecture1/1.0.png" width="500" height="300">

We can identify three different points of view: *microscopic*, *mesoscopic* and *macroscopic*; which are intimately related to the adopted lenght scale.