Recurrent Neural Networks provides the ability to process variable-length inputs and outputs of discrete or continuous data.
While the previous feedforward networks were able to process only one input to one output (one-to-one scheme), recurrent neural nets introduced in this chapter offered the possibility to make conversions between variable-length and fixed-length representations adding new operating schemes for deep learning input/output: one-to-many, many-to-many, or many-to-one.
The range of applications of RNN is wide. For this reason, we'll study them more in depth in the further chapters, in particular how to enhance the predictive power of these three modules or how to combine them to build multi-modal, question-answering, or translation applications.
In particular, in the next chapter, we'll see a practical example using text embedding and recurrent networks for sentiment analysis. This time, there will also be an opportunity to review these recurrence units under another...
