Data Science and Machine Learning (Theory and Projects) A to Z - RNN Architecture: Fixed Length Memory Model Exercise So

The video tutorial explains the concept of running average, which is used to compute the average of data that arrives in a stream. It starts with a basic example of calculating the average of a static set of numbers and then demonstrates how to update this average when new data points are added. The tutorial introduces a general formula for calculating the running average without recomputing the entire dataset, making it efficient for large data streams. The video concludes by mentioning a follow-up video that will relate running averages to sequence modeling.

What is the significance of the total number of data points in computing the running average?

How does the running average relate to sequence modeling as mentioned in the text?