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Hands-On Machine Learning with ML.NET

You're reading from   Hands-On Machine Learning with ML.NET Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#

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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781789801781
Length 296 pages
Edition 1st Edition
Languages
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Author (1):
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Jarred Capellman Jarred Capellman
Author Profile Icon Jarred Capellman
Jarred Capellman
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Fundamentals of Machine Learning and ML.NET
2. Getting Started with Machine Learning and ML.NET FREE CHAPTER 3. Setting Up the ML.NET Environment 4. Section 2: ML.NET Models
5. Regression Model 6. Classification Model 7. Clustering Model 8. Anomaly Detection Model 9. Matrix Factorization Model 10. Section 3: Real-World Integrations with ML.NET
11. Using ML.NET with .NET Core and Forecasting 12. Using ML.NET with ASP.NET Core 13. Using ML.NET with UWP 14. Section 4: Extending ML.NET
15. Training and Building Production Models 16. Using TensorFlow with ML.NET 17. Using ONNX with ML.NET 18. Other Books You May Enjoy

Additional ideas for improvements

Now that we have completed our deep dive, there are a couple of additional elements to possibly further enhance the application. A few ideas are discussed here.

Single-download optimization

Currently, when a new URL is entered or the page is changed in the WebView UWP control, the navigation is halted until a classification can be made. When this occurs—as we detailed previously—with the use of the HtmlAgilityPack library, we download and extract the text. If the page is deemed to be clean (as one would more than likely encounter the majority of the time), we would effectively be downloading the content twice. An optimization here would be to store the text in the application's sandbox storage once classification is done, then point the WebView object to that stored content. In addition, if this approach is used, add a purge background worker to remove older data so that your end users don't end up with several gigabytes of web...

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