Applications of RNNs

So far, we have only talked about one-to-one-mapped RNNs, where the current output depends on the current input as well as the previously observed history of inputs. This means that there exists an output for the sequence of previously observed inputs and the current input. However, in the real word, there can be situations where there is only one output for a sequence of inputs, a sequence of outputs for a single input, and a sequence of outputs for a sequence of inputs where the sequence sizes are different. In this section, we will look at several different settings of RNN models and the applications they would be used in.

One-to-one RNNs

In one-to-one RNNs, the current input depends on the previously observed inputs (see Figure 6.8). Such RNNs are appropriate for problems where each input has an output, but the output depends both on the current input and the history of inputs that led to the current input. An example of such a task is stock market...