Programming ML.NET
- Length: 256 pages
- Edition: 1
- Language: English
- Publisher: Microsoft Press
- Publication Date: 2022-03-15
- ISBN-10: 0137383657
- ISBN-13: 9780137383658
- Sales Rank: #4929235 (See Top 100 Books)
With .NET 5’s ML.NET and Programming ML.NET, any Microsoft .NET developer can solve serious machine learning problems, increasing their value and competitiveness in some of today’s fastest-growing areas of software development. World-renowned Microsoft development expert Dino Esposito covers everything you need to know about ML.NET, the machine learning pipeline, and real-world machine learning solutions development.
Modeled on his popular Programming ASP.NET books, this guide takes the same scenario-based approach Microsoft’s team used to build the ML.NET framework itself. Esposito presents and illuminates ML.NET’s dedicated mini-frameworks (“ML Tasks”) for specific classes of problems, and draws on personal experience to help developers apply these in the real world, where a problem’s complexity can vary widely based on data availability or the specific results you need. In a full section on ML.NET neural networks, Esposito introduces key concepts and presents realistic examples you can reuse in your own applications. Along the way, Esposito also shows how to leverage powerful Python-based machine learning tools in the .NET environment.
Programming ML.NET will help you add machine learning and artificial intelligence to your tool belt, whether you have a background in these high-demand technologies or not.
Cover Page Title Page Copyright Page Dedication Page Contents Contents at a Glance Acknowledgments Introduction Who Should Read This Book? Who Should Not Read This Book? Organization of This Book System Requirements Code Samples Errata, updates, & book support Stay in Touch Chapter 1. Artificially Intelligent Software How We Ended Up with Software The Role of Software Today AI Is Just Software Chapter 2. An Architectural Perspective of ML.NET Life Beyond Python Introducing ML.NET Consuming a Trained Model Summary Chapter 3. The Foundation of ML.NET On the Way to Data Engineering The Data to Start From The Training Step Consuming the Model from a Client Application Summary Chapter 4. Prediction Tasks The Pipeline and the Chain of Estimators The Regression ML Task Using the Regression Task The ML Devil’s Advocate Summary Chapter 5. Classification Tasks The Binary Classification ML Task Binary Classification for Sentiment Analysis The Multiclass Classification ML Task Using the Multiclass Classification Task The ML Devil’s Advocate Summary Chapter 6. Clustering Tasks The Clustering ML Task The ML Devil’s Advocate Summary Chapter 7. Anomaly Detection Tasks What Is an Anomaly? General Approaches to Detect Anomalies The Anomaly Detection ML Task The ML Devil’s Advocate Summary Chapter 8. Forecasting Tasks Predicting the Future The Forecast ML Task The ML Devil’s Advocate Summary Chapter 9. Recommendation Tasks Inside Information Retrieval Systems The ML Recommendation Task ML Devil’s Advocate Summary Chapter 10. Image Classification Tasks Transfer Learning Transfer Learning via Composition The ML Image Classification Task The ML Devil’s Advocate Summary Chapter 11. Overview of Neural Networks Feed-forward Neural Networks More Sophisticated Neural Networks Summary Chapter 12. A Neural Network to Recognize Passports Using Azure Cognitive Services Crafting Your Own Neural Network The ML Devil’s Advocate Summary Appendix A. Model Explainability Software Intelligence The Super Theory of Artificial Intelligence Machine Learning Black Boxes Interpretability and Explainability Explainability Techniques Conclusion Index Code Snippets
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