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Artificial Intelligence with Power BI

You're reading from   Artificial Intelligence with Power BI Take your data analytics skills to the next level by leveraging the AI capabilities in Power BI

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801814638
Length 348 pages
Edition 1st Edition
Languages
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Author (1):
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Mary-Jo Diepeveen Mary-Jo Diepeveen
Author Profile Icon Mary-Jo Diepeveen
Mary-Jo Diepeveen
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Table of Contents (18) Chapters Close

Preface 1. Part 1: AI Fundamentals
2. Chapter 1: Introducing AI in Power BI FREE CHAPTER 3. Chapter 2: Exploring Data in Power BI 4. Chapter 3: Data Preparation 5. Part 2: Out-of-the-Box AI Features
6. Chapter 4: Forecasting Time-Series Data 7. Chapter 5: Detecting Anomalies in Your Data Using Power BI 8. Chapter 6: Using Natural Language to Explore Data with the Q&A Visual 9. Chapter 7: Using Cognitive Services 10. Chapter 8: Integrating Natural Language Understanding with Power BI 11. Chapter 9: Integrating an Interactive Question and Answering App into Power BI 12. Chapter 10: Getting Insights from Images with Computer Vision 13. Part 3: Create Your Own Models
14. Chapter 11: Using Automated Machine Learning with Azure and Power BI 15. Chapter 12: Training a Model with Azure Machine Learning 16. Chapter 13: Responsible AI 17. Other Books You May Enjoy

Understanding the logic behind anomaly detection

The algorithm behind the anomaly detection feature in Power BI has been developed by Microsoft and is designed for real-time time-series data in many different applications. The purpose of this section is not to go over all technical details but instead to keep it pragmatic. In this section, we will explore the key considerations of this algorithm that help us understand how to use the feature in Power BI appropriately. For more in-depth information, you can read the paper Time-Series Anomaly Detection Service at Microsoft by Ren et al., 2019.

The algorithms behind Microsoft's anomaly detection feature

First of all, the algorithm created by Microsoft is a combination of two methods and is referred to as SR-CNN. SR stands for Spectral Residual and CNN stands for Convolutional Neural Network. Both of these deep learning (DL) methods are most often used for analyzing images, and it's the combination of these two techniques...

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