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 99% 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 networks, and DL4J hyperparameters/network tuning.
This is, more or less, the end of our little journey in developing ML projects using Scala and different open source frameworks. Throughout these chapters, I have tried to provide you with several examples of how to use these wonderful technologies efficiently for developing ML projects. While writing this book, I had to keep many constraints in my mind; for example, the page count, API availability, and of course, my expertise...
