Exponentially Weighted Moving Average or Exponential Weighted Average | Deep Learning

Exponentially Weighted Moving Average is a very important concept to understand Optimization in Deep Learning. It means that as we move forward, we simultaneously calculate the average of the points. In Exponentially Weighted Moving Average, we consider a few points, calculate their approximate weighted average, and then plot the graph. Then consider the next point as we move forward in time, calculate its approximate weighted average of the new set of points, and then again plot the graph and so on.

The catch here is that we are calculating the weighted average, and it means that, we give more weight to some points and less weight to others.

Optimization in Deep Learning like Momentum, RMSProp, and Adam can only be implemented with the help of Exponentially Weighted Moving Average. Thus it is very important to understand it.

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⌚Time Stamps⌚

00:00 - Intro
00:43 - What is EWMA?
05:13 - Mathematical Formulation
13:08 - Mathematical Intuition
16:48 - Python Code Demo
18:39 - Outro