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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

Product type Paperback

Published in Aug 2018

Publisher Packt

ISBN-13 9781788992893

Length 378 pages

Edition 2nd Edition

Languages

R

Tools

H2O

Concepts

Deep Learning

Authors (2):

Hodnett

Wiley

View More author details

Table of Contents (17) Chapters

Title Page

Packt Upsell

Contributors

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

1. Other Books You May Enjoy

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Index

Summary

In this chapter, we developed some TensorFlow models. We looked at TensorBoard, which is a great tool for visualizing and debugging deep learning models. We built a couple of models using TensorFlow, including a basic regression model and a Lenet model for computer vision models. From these examples, we saw that programming in TensorFlow was more complicated and error-prone than using the higher-level APIs (MXNet and Keras) that we used elsewhere in this book.

We then moved onto using TensorFlow estimators, which is a much easier interface than using TensorFlow. We then used that script in another package called tfruns, which stands for TensorFlow runs. This package allows us to call a TensorFlow estimators or Keras script with different flags each time. We used this for hyper-parameter selection, running, and evaluating multiple models. The TensorFlow runs have excellent integration with RStudio and we were able to view summaries for each run and compare runs to see the difference...

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Authors (2)

Hodnett

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

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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