Programming ML.NET: Developer Reference
Autor Dino Esposito, Francesco Espositoen Limba Engleză Paperback – 23 mai 2022
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Specificații
ISBN-13: 9780137383658
ISBN-10: 0137383657
Pagini: 256
Dimensiuni: 185 x 228 x 15 mm
Greutate: 0.46 kg
Editura: Pearson Education (US)
Seria Developer Reference
ISBN-10: 0137383657
Pagini: 256
Dimensiuni: 185 x 228 x 15 mm
Greutate: 0.46 kg
Editura: Pearson Education (US)
Seria Developer Reference
Notă biografică
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.
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.
Cuprins
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
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