Due to the success of deep learning and its growing job market, students and
researchers from many areas are getting interested in learning about deep
learning technologies. Visualization has proven to be of great help during this
learning process, while most current educational visualizations are targeted
towards one specific architecture or use case. Unfortunately, recurrent neural
networks (RNNs), which are capable of processing sequential data, are not
covered yet, despite the fact that tasks on sequential data, such as text and
function analysis, are at the forefront of deep learning research. Therefore,
we propose exploRNN, the first interactively explorable, educational
visualization for RNNs. exploRNN allows for interactive experimentation with
RNNs, and provides in-depth information on their functionality and behavior
during training. By defining educational objectives targeted towards
understanding RNNs, and using these as guidelines throughout the visual design
process, we have designed exploRNN to communicate the most important concepts
of RNNs directly within a web browser. By means of exploRNN, we provide an
overview of the training process of RNNs at a coarse level, while also allowing
detailed inspection of the data-flow within LSTM cells. Within this paper, we
motivate our design of exploRNN, detail its realization, and discuss the
results of a user study investigating the benefits of exploRNN