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 a demo project for predicting stock prices for five categories: OPEN, CLOSE, LOW, HIGH, and VOLUME. However, our approach cannot generate an actual signal. Still, it gives some idea of how to use LSTM. I know there are some serious drawbacks of this approach. Nevertheless, we did not use enough data, which potentially limits the performance of such a model.

In the next chapter, we will see how to apply deep learning approaches to a video dataset. We will describe how to process and extract features from a large collection of video clips. Then we will make the overall pipeline scalable and faster by distributing the training on multiple devices (CPUs and GPUs), and run them in parallel.

We will see a complete example of how to develop a deep learning application that accurately classifies a large collection of a video dataset, such...