Data Science and Machine Learning (Theory and Projects) A to Z - RNN Architecture: Infinite Memory Architecture Exercise

The video tutorial explains how a simple recurrent neural network (RNN) can compute the running average of real numbers in a data stream. It discusses the flexibility in choosing weights, such as alpha and beta, which do not need to follow a specific formula. The RNN structure is designed to blend historical data with current data points, effectively computing an average. The tutorial emphasizes the adaptability of RNNs in processing sequential data.

What is the primary function of the simple recurrent neural network discussed in the text?

How does the recurrent neural network compute the running average?

What are the values of alpha and beta in the context of the running average?

Can the weights in the recurrent neural network be different from the specified alpha and beta values? Explain.

What does the recurrent neural network blend to compute the running average?