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)

Frequently asked questions (FAQs)

Now that we have solved the sentiment analysis problem with an acceptable level of accuracy, there are other practical aspects of this problem and overall deep learning phenomena that need to be considered too. In this section, we will see some frequently asked questions that might already be on your mind. Answers to these questions can be found in Appendix A:

  1. I understand that the predictive accuracy of sentiment analysis using LSTM is still reasonable. However, it does not perform well on the Sentiment labeled dataset. Did our network overfit? Is there any way to observe how the training went?
  2. Considering a huge number of review texts, can we perform the training on the GPU?
  3. In relation to question 2, can we even undertake the whole process using Spark?
  4. Where can I get more training datasets for sentiment analysis?
  5. Instead of downloading the...