In this chapter, we saw how to classify cancer patients on the basis of tumor types from a very-high-dimensional gene expression dataset curated from TCGA. Our LSTM architecture managed to achieve 100% accuracy, which is outstanding. Nevertheless, we discussed many aspects of DL4J, which will be helpful in upcoming chapters. Finally, we saw answers to some frequent questions related to this project, LSTM network, and DL4J hyperparameters/nets tuning.
In the next chapter, we will see how to develop an end-to-end project for handling a multilabel (each entity can belong to multiple classes) image classification problem using CNN based on Scala and the DL4J framework on real Yelp image datasets. We will also discuss some theoretical aspects of CNNs before getting started. Nevertheless, we will discuss how to tune hyperparameters for better classification results.
...
