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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

TensorFlow models

In this section, we will use TensorFlow to build some machine learning models. First, we will build a simple linear regression model and then a convolutional neural network model, similar to what we have seen in Chapter 5, Image Classification Using Convolutional Neural Networks.

The following code loads the TensorFlow library. We can confirm it loaded successfully by setting and accessing a constant string value:

> library(tensorflow)

# confirm that TensorFlow library has loaded
> sess=tf$Session()
> hello_world <- tf$constant('Hello world from TensorFlow')
> sess$run(hello_world)
b'Hello world from TensorFlow'

Linear regression using TensorFlow

In this first Tensorflow example...

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