What process allows recurrent layers in a recurrent neural network (RNN) to be visualized in a time sequence?

The process that allows recurrent layers in a recurrent neural network (RNN) to be visualized in a time sequence is unrolling. Unrolling involves expanding the recurrent network into a sequence of layers that represent the steps taken through time, effectively illustrating how the network processes inputs sequentially. By unrolling the RNN, each time step is represented as a separate layer, showcasing the connections from one time step to another, which is crucial for understanding how the RNN maintains information and sequence dependencies over time.

This visualization aids in comprehending how the hidden states are carried forward as the layers represent the recurrent nature of the network. It also helps in debugging and designing RNN architectures since one can clearly see how inputs at different times influence the outputs and hidden states.

The other choices refer to different concepts in RNNs. For instance, embedding pertains to transforming categorical variables into continuous vector spaces, useful for input representation. Backpropagation through time is a training technique that focuses on updating weights in an RNN by considering how errors propagate back across the unrolled network but does not relate directly to visualization. Gated recurrent units are a type of recurrent layer designed to handle vanishing gradient problems, thereby improving memory but again, without directly addressing the concept