MLlib is Spark’s machine learning (ML) library. It’s goal is to make practical machine learning scalable and easy. I tried to make a complete step by step classification example using the Iris flower data set  using the BeakerX Jupyter kernel which covers the following steps Setup Data Preparation Testing and Prediction Validation The example is written in Scala but you could use any other language which is supported by the JVM. My example can be found the this GIST Read more…

Shogun is and open-source machine learning library that offers a wide range of efficient and unified machine learning methods. It is implemented in C++ and provides the necessary java integration so that it can be used in any language which is based on the JVM: – Java – Scala – Groovy – Kotlin – etc I tried to make Shogun easier to use in a JVM environment. The documentation how to use Shogun e.g. in Read more…

A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a sequence. This allows it to exhibit temporal dynamic behavior for a time sequence. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs. I am showing a basic implementation of a RNN in DL4J. Further information can be found at https://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent. This demo has been implemented in Read more…

I am a big fan of Keras – Fortunately we also have a similar framework available when we need to implement a solution which works in the JVM: DeepLearning4J. In this Blog I give a quick introduction of some of the key concepts which are needed to get started. I am using the Iris dataset to demonstrate the classification of data with the help of a neural network. This demo has been implemented in Scala Read more…