Programming ML.NET
Addison Wesley (Verlag)
978-0-13-738365-8 (ISBN)
ML.NET brings the power of machine learning to all .NET developers— and Programming ML.NET helps you apply it in real production solutions. Modeled on Dino Esposito’s best-selling Programming ASP.NET, this book takes the same scenario-based approach Microsoft’s team used to build ML.NET itself. After a foundational overview of ML.NET’s libraries, the authors illuminate mini-frameworks (“ML Tasks”) for regression, classification, ranking, anomaly detection, and more. For each ML Task, they offer insights for overcoming common real-world challenges. Finally, going far beyond shallow learning, the authors thoroughly introduce ML.NET neural networking. They present a complete example application demonstrating advanced Microsoft Azure cognitive services and a handmade custom Keras network— showing how to leverage popular Python tools within .NET.
14-time Microsoft MVP Dino Esposito and son Francesco Esposito show how to:
Build smarter machine learning solutions that are closer to your user’s needs
See how ML.NET instantiates the classic ML pipeline, and simplifies common scenarios such as sentiment analysis, fraud detection, and price prediction
Implement data processing and training, and “productionize” machine learning–based software solutions
Move from basic prediction to more complex tasks, including categorization, anomaly detection, recommendations, and image classification
Perform both binary and multiclass classification
Use clustering and unsupervised learning to organize data into homogeneous groups
Spot outliers to detect suspicious behavior, fraud, failing equipment, or other issues
Make the most of ML.NET’s powerful, flexible forecasting capabilities
Implement the related functions of ranking, recommendation, and collaborative filtering
Quickly build image classification solutions with ML.NET transfer learning
Move to deep learning when standard algorithms and shallow learning aren’t enough
“Buy” neural networking via the Azure Cognitive Services API, or explore building your own with Keras and TensorFlow
Dino Esposito is CTO and co-founder of Crionet, a company that provides innovative software and technology to professional sports organizations. A 16-time Microsoft MVP, he has authored 20+ books, including Introducing Machine Learning; and the Microsoft Press best-seller Microsoft .NET: Architecting Applications for the Enterprise. Francesco Esposito holds a degree in Mathematics, is the co-author of Introducing Machine Learning, and lives suspended between the depth of advanced mathematics and the intrigue of data science. He currently serves as the Head of Engineering and Data at Crionet. As an entrepreneur he founded Youbiquitous, a data analysis and software factory, and KBMS Data Force, a startup in Digital Therapy and intelligent healthcare.
CHAPTER 1 Artificially Intelligent Software
CHAPTER 2 An Architectural Perspective of ML.NET
CHAPTER 3 The Foundation of ML.NET
CHAPTER 4 Prediction Tasks
CHAPTER 5 Classification Tasks
CHAPTER 6 Clustering Tasks
CHAPTER 7 Anomaly Detection Tasks
CHAPTER 8 Forecasting Tasks
CHAPTER 9 Recommendation Tasks
CHAPTER 10 Image Classification Tasks
CHAPTER 11 Overview of Neural Networks
CHAPTER 12 A Neural Network to Recognize Passports
APPENDIX A Model Explainability
| Erscheinungsdatum | 25.02.2021 |
|---|---|
| Reihe/Serie | Developer Reference |
| Verlagsort | Boston |
| Sprache | englisch |
| Maße | 186 x 230 mm |
| Gewicht | 460 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Informatik ► Software Entwicklung | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 0-13-738365-7 / 0137383657 |
| ISBN-13 | 978-0-13-738365-8 / 9780137383658 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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