At its core, this new extension focuses on bringing you a more RapidMiner-native Deep Learning experience. The initial release gets you started with ExamplesSets and explores textual data, running on CPU or GPU as you like.
With this new extension the entry hurdle of creating your first Deep Learning model is lower than before; there is no need of installing anything but the extension (download it here)! There is no need to set up input shapes! And there is no need for an extra operator to apply your model on ExampleSets! There are more VISUALS, more GUIDANCE, and more SPEED due to a java-based execution!
More visual layer architecture, thanks to explanatory and individual icons per layer. Sample processes include guidance on different architectures, how to handle various data types, how to optimize networks, and learning how to use this operator:
The new Deep Learning extension provides an updateable model. This feature allows to continue training further on. So if the training took a while and new data is present, just continue training with it instead of having to start all over again. A growing number of tutorial processes, including tips and tricks.
To speed up things with big data sets, execution on GPU is also available. You'll find the switch in the general settings:
Deep Learning techniques require numerical data, so conversion methods for text are needed. A first operator for text-to-numerical conversion is now included: The extension is powered by the open-source library DeepLearning4J (version 1.0.0-beta). Check out their website and github.
We're just getting started with this native integration, so stay tuned for more features and types of data being easily usable for Deep Learning in RapidMiner!