Book Image

Analytics for the Internet of Things (IoT)

By : Andrew Minteer
5 (1)
Book Image

Analytics for the Internet of Things (IoT)

5 (1)
By: Andrew Minteer

Overview of this book

We start with the perplexing task of extracting value from huge amounts of barely intelligible data. The data takes a convoluted route just to be on the servers for analysis, but insights can emerge through visualization and statistical modeling techniques. You will learn to extract value from IoT big data using multiple analytic techniques. Next we review how IoT devices generate data and how the information travels over networks. You’ll get to know strategies to collect and store the data to optimize the potential for analytics, and strategies to handle data quality concerns. Cloud resources are a great match for IoT analytics, so Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail next. Geospatial analytics is then introduced as a way to leverage location information. Combining IoT data with environmental data is also discussed as a way to enhance predictive capability. We’ll also review the economics of IoT analytics and you’ll discover ways to optimize business value. By the end of the book, you’ll know how to handle scale for both data storage and analytics, how Apache Spark can be leveraged to handle scalability, and how R and Python can be used for analytic modeling.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Deep learning


Deep learning is an area of data science that is experiencing rapid advancement and generating a lot of excitement. Some deep learning models are better at certain types of image recognition than humans. When stories in the media mention artificial intelligence, they are usually referring to deep learning models.

Deep learning models are very complex although several of the concepts are similar to the ML concepts we have discussed so far in this chapter (such as the bias-variance tradeoff). Deep learning models can have millions of features and can take days or weeks to train.

Use cases for deep learning with IoT data

Deep learning can do wonders for complex data, with thousands to millions of features and a large history of labeled examples to use as training sets. The rapid advancements in image recognition has as much to do with the vast trove of identified images that Google and others have collected over the years, as to the advances in the deep learning algorithms used.

For...