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Java Deep Learning Projects

You're reading from Java Deep Learning Projects

Product type Book

Published in Jun 2018

Publisher Packt

ISBN-13 9781788997454

Pages 436 pages

Edition 1st Edition

Languages

Concepts

Table of Contents (13) Chapters

Preface

1. Getting Started with Deep Learning

2. Cancer Types Prediction Using Recurrent Type Networks

3. Multi-Label Image Classification Using Convolutional Neural Networks

4. Sentiment Analysis Using Word2Vec and LSTM Network

5. Transfer Learning for Image Classification

6. Real-Time Object Detection using YOLO, JavaCV, and DL4J

7. Stock Price Prediction Using LSTM Network

8. Distributed Deep Learning – Video Classification Using Convolutional LSTM Networks

9. Playing GridWorld Game Using Deep Reinforcement Learning

10. Developing Movie Recommendation Systems Using Factorization Machines

11. Discussion, Current Trends, and Outlook

12. Other Books You May Enjoy

Leave a review - let other readers know what you think

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...

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