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R Deep Learning Essentials

You're reading from   R Deep Learning Essentials A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781788992893
Length 378 pages
Edition 2nd Edition
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Authors (2):
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Joshua F. Wiley Joshua F. Wiley
Author Profile Icon Joshua F. Wiley
Joshua F. Wiley
Mark Hodnett Mark Hodnett
Author Profile Icon Mark Hodnett
Mark Hodnett
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. Training a Prediction Model FREE CHAPTER 3. Deep Learning Fundamentals 4. Training Deep Prediction Models 5. Image Classification Using Convolutional Neural Networks 6. Tuning and Optimizing Models 7. Natural Language Processing Using Deep Learning 8. Deep Learning Models Using TensorFlow in R 9. Anomaly Detection and Recommendation Systems 10. Running Deep Learning Models in the Cloud 11. The Next Level in Deep Learning 12. Other Books You May Enjoy

Deep Learning Models Using TensorFlow in R

This chapter is about using TensorFlow in R. We have already used TensorFlow quite a lot, as Keras is a high-level neural network API that uses either TensorFlow, CNTK, or Theano. In R, Keras uses TensorFlow in the background. TensorFlow is more difficult to develop deep learning models in. However, there are two interesting packages in TensorFlow that could be overlooked: TensorFlow estimators and TensorFlow runs. We will cover both of these packages in this chapter.

In this chapter, we will cover the following topics:

  • Introduction to TensorFlow
  • Building models using TensorFlow
  • TensorFlow estimators
  • TensorFlow runs packages
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