Throughout Chapter 2, Scala for Regression Analysis, to Chapter 6, Scala for Recommender System, we have learned about linear and classic machine learning (ML) algorithms through real-life examples. In this chapter, we will explain some basic concepts of deep learning (DL). We will start with DL, which is one of the emerging branches of ML. We will briefly discuss some of the most well-known and widely used neural network architectures and DL frameworks and libraries.
Finally, we will use the Long Short-Term Memory (LSTM) architecture for cancer type classification from a very high-dimensional dataset curated from The Cancer Genome Atlas (TCGA). The following topics will be covered in this chapter:
- DL versus ML
- DL and neural networks
- Deep neural network architectures
- DL frameworks
- Getting started with learning
