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R Deep Learning Projects

You're reading from R Deep Learning Projects Master the techniques to design and develop neural network models in R

Product type Paperback

Published in Feb 2018

Publisher Packt

ISBN-13 9781788478403

Length 258 pages

Edition 1st Edition

Languages

R

Tools

Keras

Concepts

Deep Learning

Table of Contents (11) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Handwritten Digit Recognition Using Convolutional Neural Networks FREE CHAPTER

2. Traffic Sign Recognition for Intelligent Vehicles

3. Fraud Detection with Autoencoders

4. Text Generation Using Recurrent Neural Networks

5. Sentiment Analysis with Word Embeddings

1. Other Books You May Enjoy

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Index

Getting ready

In this chapter, we will introduce keras and tensorflow for R. keras is a model-level building, in that it provides a high-level interface to quickly develop deep learning models. Instead of implementing low-level operations such as convolutions and tensor products, it relies on Theano, TensorFlow or CNTK in the backend, and according to the development team, more backends would be supported in the future.

Why do you need a backend? Well, if the computation becomes more complicated, which is often the case in deep learning, you need to use different computation methods (known as computation graphs) and hardware (GPUs). For instructional purposes, all our sample codes run without GPU.

Installing Keras and TensorFlow for R

As per the official documentation, you can install Keras simply with:

devtools::install_github("rstudio/keras")

The Keras R interface uses tensorflow as a backend engine by default. To install both the core keras library and tensorflow, then do:

library(keras)...

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