Data Science and Machine Learning (Theory and Projects) A to Z - RNN Architecture: ManyToMany Model Exercise 01

The video tutorial explains the many-to-many architecture of Recurrent Neural Networks (RNNs), where the input and output lengths are equal. It discusses how inputs and outputs are represented as vectors and provides examples of scenarios where this architecture is applicable. The tutorial emphasizes that while each input-output pair has equal lengths, different pairs can have varying lengths.

What is a key characteristic of a many-to-many RNN architecture?

• The input and output lengths are always different.

• The input length is always greater than the output length.

• The input and output lengths are equal.

• The output length is always greater than the input length.

In the context of RNNs, what does the notation TXI = tyi signify?

• The input and output vectors are unrelated.

• The input vectors are always longer than the output vectors.

• The input and output vectors have different lengths.

• The input and output vectors have equal lengths.

How are the input and output vectors represented in a many-to-many RNN?

• As multiple vectors with different numbers for each input-output pair.

• As a single vector for all inputs and outputs.

• As a single vector for each input-output pair.

• As multiple vectors with equal numbers for each input-output pair.

What is a unique feature of input instances in a many-to-many RNN architecture?

• All input instances must have the same length.

• Different input instances can have varying lengths.

• Different input instances must have the same length.

• All input instances must be longer than output instances.

In a many-to-many RNN, how do the lengths of input and output pairs compare?

• The output is always longer.

• The input is always longer.

• They are always equal.

• They are always different.