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R Deep Learning Essentials. - Second Edition

You're reading from  R Deep Learning Essentials. - Second Edition

Product type Book
Published in Aug 2018
Publisher Packt
ISBN-13 9781788992893
Pages 378 pages
Edition 2nd Edition
Languages
Concepts
Authors (2):
Mark Hodnett Mark Hodnett
Profile icon Mark Hodnett
LinkedIn
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.
See other products by Mark Hodnett
Joshua F. Wiley Joshua F. Wiley
Profile icon Joshua F. Wiley
LinkedIn
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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Table of Contents (13) Chapters

Preface Getting Started with Deep Learning Training a Prediction Model Deep Learning Fundamentals Training Deep Prediction Models Image Classification Using Convolutional Neural Networks Tuning and Optimizing Models Natural Language Processing Using Deep Learning Deep Learning Models Using TensorFlow in R Anomaly Detection and Recommendation Systems Running Deep Learning Models in the Cloud The Next Level in Deep Learning Other Books You May Enjoy

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)

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Profile icon Mark Hodnett
LinkedIn
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.
See other products by Mark Hodnett
Profile icon Joshua F. Wiley
LinkedIn
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.
Read more
See other products by Joshua F. Wiley
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