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: Yes, of course, you can. However, please note that you have to provide a sufficient number of images, preferably at least a few thousand images for each animal type. Otherwise, the model will not be trained well.

Answer to question 2: A possible reason could be you are trying to feed all the images at once or you are training on CPU (and your machine does not have a good configuration). The former can be addressed easily; we can undertake the training in batch mode, which is recommended for the era of deep learning.

The latter case can be addressed by migrating your training from CPU to GPU. However, if your machine does not have a GPU, you can try migrating to Amazon GPU instance to get the support for a single (p2.xlarge) or multiple GPUs (for example, p2.8xlarge).

Answer to question 3: The application provided should be enough to understand...