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Artificial Intelligence for IoT Cookbook

You're reading from   Artificial Intelligence for IoT Cookbook Over 70 recipes for building AI solutions for smart homes, industrial IoT, and smart cities

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Product type Paperback
Published in Mar 2021
Publisher Packt
ISBN-13 9781838981983
Length 260 pages
Edition 1st Edition
Languages
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Author (1):
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Michael Roshak Michael Roshak
Author Profile Icon Michael Roshak
Michael Roshak
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Toc

Table of Contents (11) Chapters Close

Preface 1. Setting Up the IoT and AI Environment 2. Handling Data FREE CHAPTER 3. Machine Learning for IoT 4. Deep Learning for Predictive Maintenance 5. Anomaly Detection 6. Computer Vision 7. NLP and Bots for Self-Ordering Kiosks 8. Optimizing with Microcontrollers and Pipelines 9. Deploying to the Edge 10. About Packt

Sample co-variance and correlation

Co-variance measures the joint variability change of two sensors with respect to each other. Positive numbers would indicate that the sensors are reporting the same data. Negative numbers indicate that there is an inverse relationship between the sensors. The co-variance of two sensors can be calculated using the DataFrame stat.cov function in a Spark DataFrame:

df.stat.cov('averageRating', 'numVotes')
You have been reading a chapter from
Artificial Intelligence for IoT Cookbook
Published in: Mar 2021
Publisher: Packt
ISBN-13: 9781838981983
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