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?

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

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