Wiley Joshua F - R Deep Learning Essentials: A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet

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First published: March 2016
Second edition: August 2018

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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.

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 post doctoral 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.

Vitor Bianchi Lanzetta is an economist with a masters in applied economics from the University of So Paulo, one of the most reputable universities in Latin America. He is very passionate about data science and has done academic research using neural networks, and he has co-authored Hands-On Data Science with R. He also authored R Data Visualization Recipes and reviewed Mike Bernicos Deep Learning Quick Reference.

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Deep learning is probably the hottest technology in data science right now, and R is one of the most popular data science languages. However, R is not considered as an option for deep learning by many people, which is a shame, as R is a wonderful language for data science. This book shows that R is a viable option for deep learning, because it supports libraries such as MXNet and Keras.

When I decided to write this book, I had numerous goals. First, I wanted to show how to apply deep learning to various tasks, and not just to computer vision and n atural language processing. This book covers those topics, but it also shows how to use deep learning for prediction, regression, anomaly detection, and recommendation systems. The second goal was to look at topics in deep learning that are not covered well elsewhere; for example, interpretability with LIME, deploying models, and using the cloud for deep learning. The last goal was to give an overall view of deep learning and not just provide machine learning code. I think I achieved this by discussing topics such as how to create datasets from raw data, how to benchmark models against each other, how to manage data when model building, and how to deploy your models. My hope is that by the end of this book, you will also be convinced that R is a valid choice for use in deep learning.

If you have some experience with R and are looking for a book that shows some practical examples of how to use R for deep learning, this is the book for you! This book assumes that you are familiar with some of the concepts in machine learning, such as splitting data into train and test sets. Anyone who has built machine learning algorithms in R should have no problem with this book.