FXApps

Weka is one of the most known tools for Machine Learning in Java, which also has a great Java API including API for k-means clustering. Using JavaFX it is possible to visualize unclassified data, classify the data using Weka APIs and then visualize the result in a JavaFX chart, like the Scatter chart . In this post we will show a simple application that allows you to load data, show it without series distinction using a JavaFX scatter chart,, then we use Weka to classify the data in a defined number of clusters and finally separated the clustered data by chart series. We will be using the Iris.2D.arff file that comes with Weka download. K-means clustering using Weka is really simple and requires only a few lines of code as you can see in this post . In our application we will build 3 charts for the Iris dataset : Data without class distinction (no classes) The data with the ground truth classification Data clustered using weka As you can see the clustered data is rea...

We already talked about tensorflow and JavaFX on this blog, but tensorflow Java API is still incomplete. A mature and well documented API is DeepLearning4J . In this example we load the trained model in our application, create a canvas for writing and enter is pressed, the canvas image is resized and sent to the deeplearning4j trained model for recognition: The way it "guess" the digit is like the "hello world" for  deep neural networks. A neuron roughly mimics the human brain neuron and it has a weight, which controls when the neuron is activated. A neural network consists of many neurons linked to each other and organized in layers. What we do is provide to our neural network labeled data and adjust the weights of our neurons until it is able to correctly predict values for the given data, this is called training. Once it is trained, we test the neural network against known labeled data to measure the neural network precision (in ...