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

Deep Learning

Authors (2):

Mark Hodnett

Joshua F. Wiley

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Table of Contents (13) Chapters

Preface

1. Getting Started with Deep Learning

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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Using Google Cloud for deep learning

Google Cloud also has GPU instances. At the time of writing this book, the price of an instance with an NVIDIA Tesla K80 GPU card (which is also the GPU card in an AWS p2.xlarge instance) is $0.45 per hour on-demand. This is significantly cheaper than the AWS on-demand price. Further details of Google Cloud's GPU instances are at https://cloud.google.com/gpu/. However, for Google Cloud, we are not going to use instances. Instead, we are going to use the Google Cloud Machine Learning Engine API to submit machine learning jobs to the cloud. One big advantage of this approach over provisioning virtual machines is that you only pay for the hardware resources that you use and do not have to worry about setting up and terminating instances. More details and pricing can be found at https://cloud.google.com/ml-engine/pricing.

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Authors (2)

Mark 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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Joshua F. 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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