Applications of RNNs
Understand the four types of recurrent neural networks—one-to-one, one-to-many, many-to-one, and many-to-many—and their practical applications such as stock prediction, image captioning, sentence classification, and language translation. Learn to identify which RNN type fits specific sequence data problems.
We'll cover the following...
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’s an output for the sequence of previously observed inputs and the current input. However, in the real world, 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 lesson, we’ll look at the four settings of RNN models and the applications they would be used in.
One-to-one RNNs ...