Deep learning, i.e. the use of deep, multi-layer neural networks, is the major driver of the current machine learning boom. From great leaps in quality in automatic translation, over autonomous driving, to beating grandmasters in the game Go, this technique has made a lot of headlines.
Deeplearning4J, also called DL4J, is a Java library for Deep Learning. But, it also a whole family of other libraries that simplify the use of deep learning models with Java. As an alternative to the many Python based frameworks, DL4J offers a way to easily bring Deep Learning into existing enterprise environments.
This blog post shows how to get started with DL4J in no time. By using an example where the goal is to predict whether a customer will leave his bank, each step of a typical workflow is considered. In order to focus on the individual steps, only excerpts of the code currently being discussed are shown. Imports and other Java boilerplate are left out, but the complete code including training data can be found at https://github.com/treo/quickstart-with-dl4j.