In the previous chapters, we saw how adding depth to our neural networks helps achieve much greater results; the same is true with RNNs, where adding more layers allows us to learn even more complex information.
Now that we have seen what RNNs are and have an understanding of how they work, let's go deeper and see what deep RNNs look like and what kind of benefits we gain from adding additional layers. Going deeper into RNNs is not as straightforward as it was when we were dealing with FNNs and CNNs; we have to make a few different kinds of considerations here, particularly about how and where we should add the nonlinearity between layers.
If we want to go deeper, we can stack more hidden recurrent layers on top of each other, which allows our architecture to capture and learn complex information at multiple timescales, and before the information is passed from...
