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R Deep Learning Cookbook

You're reading from   R Deep Learning Cookbook Solve complex neural net problems with TensorFlow, H2O and MXNet

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Length 288 pages
Edition 1st Edition
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Authors (2):
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Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Author Profile Icon Achyutuni Sri Krishna Rao
Achyutuni Sri Krishna Rao
PKS Prakash PKS Prakash
Author Profile Icon PKS Prakash
PKS Prakash
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Deep Learning with R 3. Convolution Neural Network 4. Data Representation Using Autoencoders 5. Generative Models in Deep Learning 6. Recurrent Neural Networks 7. Reinforcement Learning 8. Application of Deep Learning in Text Mining 9. Application of Deep Learning to Signal processing 10. Transfer Learning

Training a deep learning model on a GPU


The Graphical processing unit (GPU) is hardware used for rendering images using a lot of cores. Pascal is the latest GPU micro architecture released by NVIDIA. The presence of hundreds of cores in GPU helps enhance the computation. This section provides the recipe for running a deep learning model using GPU.

Getting ready

This section provides dependencies required to run GPU and CPU:

  1. The experiment performed in this recipe uses GPU hardware such as GTX 1070.
  2. Install mxnet for GPU. To install mxnet for GPU for a specified machine, follow the installation instruction from mxnet.io. Select the requirement as shown in the screenshot, and follow the instructions:

Steps to get installation instruction for MXNet

How to do it...

Here is how you train a deep learning model on a GPU:

  1. The Inception-BN-transferred learning recipe discussed in the previous section can be made to run on the GPU installed and the configured machine by changing the device settings as shown...
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