Book Image

Java Deep Learning Projects

By : Md. Rezaul Karim
Book Image

Java Deep Learning Projects

By: Md. Rezaul Karim

Overview of this book

Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts. Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines. You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you’ll be able to use their features to build and deploy projects on distributed computing environments. You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks. By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems.
Table of Contents (13 chapters)

Getting Started with Deep Learning

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)