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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#

Product type Paperback

Published in Mar 2020

Publisher Packt

ISBN-13 9781789801781

Length 296 pages

Edition 1st Edition

Languages

Tools

.NET Core

Concepts

Machine Learning

Author (1):

Jarred Capellman

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Table of Contents (19) Chapters

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

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Section 4: Extending ML.NET

This section explains how to use pre-trained models in other formats with ML.NET, including TensorFlow and ONNX. In addition, some of the chapters will cover how to train at scale and the lessons learned using the DMTP project.

This section comprises the following chapters:

Chapter 11, Training and Building Production Models
Chapter 12, Using TensorFlow with ML.NET
Chapter 13, Using ONNX with ML.NET

The rest of the chapter is locked

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Authors (1)

Capellman

Jarred Capellman is a Director of Engineering at SparkCognition, a cutting-edge artificial intelligence company located in Austin, Texas. At SparkCognition, he leads the engineering and data science team on the industry-leading machine learning endpoint protection product, DeepArmor, combining his passion for software engineering, cybersecurity, and data science. In his free time, he enjoys contributing to GitHub daily on his various projects and is working on his DSc in cybersecurity, focusing on applying machine learning to solving network threats. He currently lives just outside of Austin, Texas, with his wife, Amy.

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