Learning the RNN architecture by example

As you can imagine, RNNs stand out because of their recurrent mechanism. We will start with a detailed explanation of this in the next section. We will talk about different types of RNNs after that, along with some typical applications.

Recurrent mechanism

Recall that in feedforward networks (such as vanilla neural networks and CNNs), data moves one way, from the input layer to the output layer. In RNNs, the recurrent architecture allows data to circle back to the input layer. This means that data is not limited to a feedforward direction. Specifically, in a hidden layer of an RNN, the output from the previous time point will become part of the input for the current time point. The following diagram illustrates how data flows in an RNN in general:

Figure 13.1: The general form of an RNN

Such a recurrent architecture makes RNNs work well with sequential data, including time series (such as daily temperatures, daily product...