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You're reading from Java Deep Learning Projects Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788997454

Length 436 pages

Edition 1st Edition

Languages

Java

Tools

Deeplearning4j

Concepts

Deep Learning

Author (1):

Md. Rezaul Karim

View More author details

Table of Contents (17) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Getting Started with Deep Learning FREE CHAPTER

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

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Index

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: