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 a simple recurrent neural network as discussed in the video?

• To classify images

• To compute the running average of real numbers in a stream

• To generate text

• To predict stock prices

In the context of running averages, what is true about the alpha and beta values?

• They must be equal to each other

• They must always be 1/N and N-1/N

• They are irrelevant to the computation

• They can be any values, not necessarily 1/N and N-1/N

What does the overall structure of the recurrent neural network aim to compute?

• A blend or average of historical and current data points

• A sum of all previous data points

• A minimum value from the data stream

• A maximum value from the data stream

Which of the following is NOT a characteristic of the recurrent neural network described?

• It processes data in a stream

• It requires fixed alpha and beta values

• It blends historical data with new data

• It computes a running average

How does the recurrent neural network handle new data points?

• It replaces old data with new data

• It stores them separately

• It ignores them

• It blends them with historical data