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Analytics for the Internet of Things (IoT)

You're reading from   Analytics for the Internet of Things (IoT) Intelligent analytics for your intelligent devices

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787120730
Length 378 pages
Edition 1st Edition
Languages
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Author (1):
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Andrew Minteer Andrew Minteer
Author Profile Icon Andrew Minteer
Andrew Minteer
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Toc

Table of Contents (14) Chapters Close

Preface 1. Defining IoT Analytics and Challenges FREE CHAPTER 2. IoT Devices and Networking Protocols 3. IoT Analytics for the Cloud 4. Creating an AWS Cloud Analytics Environment 5. Collecting All That Data - Strategies and Techniques 6. Getting to Know Your Data - Exploring IoT Data 7. Decorating Your Data - Adding External Datasets to Innovate 8. Communicating with Others - Visualization and Dashboarding 9. Applying Geospatial Analytics to IoT Data 10. Data Science for IoT Analytics 11. Strategies to Organize Data for Analytics 12. The Economics of IoT Analytics 13. Bringing It All Together

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...

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