Programming ML.NET /
Esposito, Dino, 1965-
Programming ML.NET / Dino Esposito, Francesco Esposito. - xx, 235 pages illustrations 24 cm
Includes index. "Professional" -- Cover.
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.
"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.
978-0-13-738365-8
2021952995
GBC1A4542 bnb
COMPUTER SOFTWARE--DEVELOPMENT
MACHINE LEARNING
QA 76.76 E87 2022
006.31
Programming ML.NET / Dino Esposito, Francesco Esposito. - xx, 235 pages illustrations 24 cm
Includes index. "Professional" -- Cover.
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.
"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.
978-0-13-738365-8
2021952995
GBC1A4542 bnb
COMPUTER SOFTWARE--DEVELOPMENT
MACHINE LEARNING
QA 76.76 E87 2022
006.31
