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Programming ML.NET / Dino Esposito, Francesco Esposito.

By: Esposito, Dino, 1965- [author]Contributor(s): Esposito, Francesco [author]Material type: Text

TextPublisher: [London] : published with the authorization of Microsoft Corporation by Pearson Education, Inc, [2022]Copyright date: ©2022Description: xx, 235 pages illustrations 24 cmISBN: 978-0-13-738365-8Subject(s): COMPUTER SOFTWARE -- DEVELOPMENT | MACHINE LEARNINGDDC classification: 006.31 LOC classification: QA 76.76 E87 2022

Contents:

Chapter 1 : Artificially intelligent software -- Chapter 2 : An architectural perspective on ML.NET -- Chapter 3 : The foundation of ML.NET -- Chapter 4 : Prediction tasks -- Chapter 5 : Classification task -- Chapter 6 : Clustering task -- Chapter 7 : Anomaly detection -- Chapter 8 : Forecasting task -- Chapter 9 : Recommendation tasks -- Chapter 10 : Image classification tasks -- Chapter 11 : Overview of neutral networks -- Chapter 12 : A neural network to recognize passports -- Apppendix A : Model explainability.

Summary: "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 ({28}ML Tasks.

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Holdings
Item type	Current library	Home library	Collection	Shelving location	Call number	Status	Date due	Barcode
Books	NU Fairview College LRC	NU Fairview College LRC	School of Engineering and Technology	General Circulation	GC QA 76.76 E87 2022 (Browse shelf(Opens below))	Available		NUFAI000005835

Includes index.

"Professional" -- Cover.

"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 ({28}ML Tasks.

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