Programming ML.NET



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Price$42.88
Rating
AuthorsDino Esposito, Francesco Esposito
PublisherMicrosoft Press
Published2022
Pages256
LanguageEnglish
FormatPaper book / ebook (PDF)
ISBN-100137383657
ISBN-139780137383658
EBook Hardcover Paperback

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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