In this chapter, we will explain some basic concepts of Machine Learning (ML) and Deep Learning (DL) that will be used in all subsequent chapters. We will start with a brief introduction to ML. Then we will move on to 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. Next, we will look at various features of deep learning frameworks and libraries. Then we will see how to prepare a programming environment, before moving on to coding with some open source, deep learning libraries such as DeepLearning4J (DL4J).
Then we will solve a very famous ML problem: the Titanic survival prediction. For this, we will use an Apache Spark-based Multilayer Perceptron (MLP) classifier to solve this problem. Finally, we'll see some frequently asked questions that will help us generalize our basic understanding of DL. Briefly, the following topics will be covered:
- A soft introduction to ML
- Artificial Neural Networks (ANNs)
- Deep neural network architectures
- Deep learning frameworks
- Deep learning from disasters—Titanic survival prediction using MLP
- Frequently asked questions (FAQ)
