The code for this chapter can be found here: https://github.com/PacktPublishing/Java-Deep-Learning-Cookbook/blob/master/08_Performing_Anomaly_detection_on_unsupervised%20data/sourceCode/cookbook-app/src/main/java/MnistAnomalyDetectionExample.java.

The JFrame-specific implementation can be found here:
https://github.com/PacktPublishing/Java-Deep-Learning-Cookbook/blob/master/08_Performing_Anomaly_detection_on_unsupervised%20data/sourceCode/cookbook-app/src/main/java/MnistAnomalyDetectionExample.java#L134.

After cloning our GitHub repository, navigate to the Java-Deep-Learning-Cookbook/08_Performing_Anomaly_detection_on_unsupervised data/sourceCode directory. Then, import the cookbook-app project as a Maven project by importing pom.xml.

Note that we use the MNIST dataset from here: http://yann.lecun.com/exdb/mnist/.

However, we don't have to download the dataset for this chapter: DL4J has a custom implementation that allows us to fetch MNIST data automatically. We will be using this in this chapter.