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

Product type Paperback

Published in Aug 2018

Publisher Packt

ISBN-13 9781788992893

Length 378 pages

Edition 2nd Edition

Languages

Tools

H2O

Concepts

Deep Learning

Authors (2):

Joshua F. Wiley

Mark Hodnett

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Table of Contents (13) Chapters

Preface

1. Getting Started with Deep Learning FREE CHAPTER

2. Training a Prediction Model

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

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TensorFlow estimators and TensorFlow runs packages

TensorFlow estimators and the TensorFlow runs packages are great packages to use for deep learning. In this section, we will use both to train a model based on our churn prediction data from Chapter 4, Training Deep Prediction Models.

TensorFlow estimators

TensorFlow estimators allow you to build TensorFlow models using a simpler API interface. In R, the tfestimators package allows you to call this API. There are different model types, including linear models and neural networks. The following estimators are available:

linear_regressor() for linear regression
linear_classifier() for linear classification
dnn_regressor() for deep neural network regression
dnn_classifier()...

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Authors (2)

Hodnett

Mark Hodnett is a data scientist with over 20 years of industry experience in software development, business intelligence systems, and data science. He has worked in a variety of industries, including CRM systems, retail loyalty, IoT systems, and accountancy. He holds a master's in data science and an MBA. He works in Cork, Ireland, as a senior data scientist with AltViz.

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Wiley

Joshua F. Wiley is a lecturer at Monash University, conducting quantitative research on sleep, stress, and health. He earned his Ph.D. from the University of California, Los Angeles and completed postdoctoral training in primary care and prevention. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. He develops or co-develops a number of R packages including Varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.

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