As mentioned in the introduction to this chapter, RNNs are used frequently for—and have brought about tremendous results in—tasks such as natural language processing, machine translation, and algorithmic trading. For these tasks, we need sequential or time-series data—that is, the data has a fixed order. For example, languages and music have a fixed order. When we speak or write sentences, they follow a framework, which is what enables us to understand them. If we break the rules and mix up words that do not correlate, then the sentence no longer makes sense.
Suppose we have the sentence The greatest glory in living lies not in never falling, but in rising every time we fall and we pass it through a sentence randomizer. The output that we get is fall. falling, in every in not time but in greatest lies The we living glory rising...
