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)

Distributed training on AWS deep learning AMI 9.0

So far, we have seen how to perform training and inferencing on a single GPU. However, to make the training even faster in a parallel and distributed way, having a machine or server with multiple GPUs is a viable option. An easy way to achieve this is by using AMAZON EC2 GPU compute instances.

For example, P2 is well suited for distributed deep learning frameworks that come with the latest binaries of deep learning frameworks (MXNet, TensorFlow, Caffe, Caffe2, PyTorch, Keras, Chainer, Theano, and CNTK) pre-installed in separate virtual environments.

An even bigger advantage is that they are fully configured with NVidia CUDA and cuDNN. Interested readers can take a look at https://aws.amazon.com/ec2/instance-types/p2/. A short glimpse of P2 instances configuration and pricing is as follows:

P2 instance details

For this project...