Book Image

Java Deep Learning Projects

Book Image

Java Deep Learning Projects

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)

Playing GridWorld Game Using Deep Reinforcement Learning

As human beings, we learn from experiences. We have not become so charming overnight or by accident. Years of compliments as well as criticism have all helped shape who we are today. We learn how to ride a bike by trying out different muscle movements until it just clicks. When you perform actions, you are sometimes rewarded immediately, and this is known as reinforcement learning (RL).

This chapter is all about designing a machine learning system driven by criticisms and rewards. We will see how to develop a demo GridWorld game using Deeplearning4j (DL4J), reinforcement learning 4j (RL4J), and Neural Q-learning that acts as the Q function. We will start from reinforcement learning and its theoretical background so that the concept is easier to grasp. In summary, the following topics will be covered in this chapter:

  • Notation...