Project 9

It works quite similarly for RNNs, but here we’ve got a little bit more going on.

• Firstly, information travels through time in RNNs, which means that information from previous time points is used as input for the next time points.
• Secondly, you can calculate the cost function, or your error, at each time point.

Basically, during the training, your cost function compares your outcomes (red circles on the image below) to your desired output.

As a result, you have these values throughout the time series, for every single one of these red circles.