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Dino Esposito, Francesco Esposito

Programming ML.NET

Auflage 1

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 students need to know about ML.NET, the machine learning pipeline, and real-world machine learning solutions development.

  • Modeled on Esposito's popular Programming ASP.NET books
  • Use the same scenario-based approach Microsoft's team used to build the ML.NET framework itself
  • Discover ML.NET's dedicated mini-frameworks (ML Tasks) for specific classes of problems
  • Draw on Esposito's personal experience to apply these problems in the real world
  • Learn key concepts and realistic examples related to ML.NET neural networks
  • Leverage powerful Python-based machine learning tools in the .NET environment

Programming ML.NET will help students add machine learning and artificial intelligence to their tool belt, whether they have a background in these high-demand technologies or not.

  • eBook (PDF)
    37,44 €

Produktdetails

Verlagsnummer: 9780137383559
ISBN: 978-0-13-738355-9
Produkttyp: eBook (PDF)
Verlag: Microsoft Press
Erscheinungsdatum: 03.02.2022
Dateigröße in MB: 4.72
Auflage: 1
Sprache: Englisch

Artikelbeschreibung

The expert guide to creating production machine learning solutions with ML.NET!

 

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 Espositos best-selling Programming ASP.NET, this book takes the same scenario-based approach Microsofts team used to build ML.NET itself. After a foundational overview of ML.NETs 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 users 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 learningbased 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.NETs 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 arent enough
  • Buy neural networking via the Azure Cognitive Services API, or explore building your own with Keras and TensorFlow

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