In this section, we will begin by taking a look into what forms an RNN can take, depending on the application it is being built for. After that, we will dive into bidirectional RNNs, and, finally, we'll end this section by looking into how RNNs can be stacked to build deep RNNs.
Different flavors of RNN
RNNs can take multiple forms, depending on the type of use case it is applied to. Let's see the various forms an RNN can take, as follows:
- One-to-one: This is the simplest form of RNN and is very similar to a traditional neural network, wherein the RNN takes in a single input and provides a single output. An example of a one-to-one RNN is shown in the following figure:
- One-to-many: In a one-to-many RNN, the network takes in only one input and produces multiple outputs. Such an RNN is used for solving problems such as music generation, wherein music is generated on the input of a single musical note. An example of a one-to-many RNN is shown in the...
