Applications of RNNs
So far what we have talked about is a one-to-one mapped RNN, 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 a few such applications.
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 prediction, where we output a value for the current input, and this output...
