At the time of writing, for training neural networks nearly everywhere, the error backpropagation algorithm is used. The result of performing inference on the training set of examples (in our case, the set of subsequences) is checked against the expected result (labeled data). The difference between the actual and expected values ​​is called an error. This error is propagated to the network weights in the opposite direction. Thus, the network adapts to labeled data, and the result of this adaptation works well for the data that the network did not meet in the initial training examples (generalization hypothesis).
In the case of a recurrent network, we have several options regarding which network outputs we can consider the error. This section describes the two main approaches: the first considers the...
