Added intuition behind exponentially weighted averages

2- Improving Deep Neural Networks/Readme.md

@@ -460,6 +460,9 @@ Implications of L2-regularization on:
     - `beta = 0.98` will average last 50 entries
     - `beta = 0.5` will average last 2 entries
 - Best beta average for our case is between 0.9 and 0.98
+- Intuition: The reason why exponentially weighted averages are useful for further optimizing gradient descent algorithm is that it
+  can give different weights to recent data points (`theta`) based on value of `beta`. If `beta` is high (around 0.9), it smoothens
+  out the averages of skewed data points (oscillations w.r.t. Gradient descent terminology). So this reduces oscillations in gradient descent and hence makes faster and smoother path towerds minima.
 - Another imagery example:   
     ![](Images/Nasdaq1_small.png)   
     _(taken from [investopedia.com](https://www.investopedia.com/))_