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)

Answers to questions

Answer to question 1: We have seen that our trained model performs pretty well on the test set with an accuracy of 87%. Now, if we see the model versus iteration score and other parameters from the following graph, then we can see that our model was not overfitted:

Model versus iteration score and other parameters of the LSTM sentiment analyzer

Now, for the sentiment labeled sentences, the trained model did not perform well. There could be several reasons for that. For example, our model is trained with only the movie review dataset, but here, we try to force our model to perform on different types of datasets too, for example, Amazon and Yelp. Nevertheless, we have not tuned the hyperparameters carefully.

Answer to question 2: Yes, in fact, this will be very helpful. For this, we have to make sure that our programming environment is ready. In other words...