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

The video tutorial explains how a recurrent neural network (RNN) can be used to compute running averages from a stream of data points. It demonstrates building a simple RNN with a single neuron and no nonlinear activation functions. The tutorial walks through the computation process, showing how the RNN maintains history and computes outputs using shared weights. The RNN is unrolled to illustrate its operations, and it is shown how the network simulates running averages, despite lacking nonlinearities.

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

• To compute the running average of a data stream

• To classify images

• To predict future stock prices

• To generate text

What is the initial value of the hidden state in the RNN setup?

• 1

• 0

• The first input value

• A random number

How does the RNN update its hidden state with each new input?

• By using a nonlinear activation function

• By multiplying the input with a weight and adding it to the previous state

• By adding a constant value

• By averaging the input with the previous state

What is a notable characteristic of the RNN described in the video?

• It requires a large number of neurons

• It is designed for image recognition

• It uses complex nonlinear activations

• It simulates running averages without nonlinear activations

Why might the RNN discussed be considered uninteresting?

• It lacks nonlinearities and complex features

• It is too complex to implement

• It requires too much computational power

• It cannot handle large datasets