The workflow builds, trains, and saves an RNN with an LSTM layer to generate new fictive mountain names. The brown nodes define the network structure. The "Pre-Processing" metanode reads original mountain names and index-encodes them. The Keras Network Learner node trains the network using index-encoded original mountain names. Finally, the trained network is prepared for deployment, transformed into a TensorFlow model, and saved to a file. In order to run the example, please make sure you have the following KNIME extensions installed: * KNIME Deep Learning - Keras Integration (Labs) * KNIME Deep Learning - TensorFlow Integration * KNIME Python Integration You also need a local Python installation that includes Keras. Please refer to https://www.knime.com/deeplearning#keras for installation recommendations and further information.