Applications of a Many-to-One Architecture RNN

In the previous chapter, we learned about the workings of RNN and LSTM. We also learned about sentiment classification, which is a classic many-to-one application, as many words in the input correspond to one output (positive or negative sentiment).

In this chapter, we will further our understanding of the many-to-one architecture RNN by going through the following recipes:

• Generating text
• Movie recommendations
• Topic-modeling using embeddings
• Forecasting the value of a stock's price