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)

Summary

In this chapter, we saw how to develop an end-to-end project that will detect objects from video frames when video clips play continuously. We saw how to utilize the pre-trained Tiny YOLO model, which is a smaller variant of the original YOLO v2 model.

Furthermore, we covered some typical challenges in object detection from both still images and videos, and how to solve them using bounding box and non-max suppression techniques. We learned how to process a video clip using the JavaCV library on top of DL4J. Finally, we saw some frequently asked questions that should be useful in implementing and extending this project.

In the next chapter, we will see how to develop anomaly detection, which is useful in fraud analytics in finance companies such as banks, and insurance and credit unions. It is an important task to grow the business. We will use unsupervised learning algorithms...