Programming ML.NET
| Price | $34.56 - $35.70
|
| Rating |  |
| Authors | Dino Esposito, Francesco Esposito |
| Publisher | Microsoft Press |
| Published | 2022 |
| Pages | 256 |
| Language | English |
| Format | Paper book / ebook (PDF) |
| ISBN-10 | 0137383657 |
| ISBN-13 | 9780137383658 |
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.
Chapter 2:
→ https://itbook.store/files/9780137383658/chapter2.pdf
Source Code:
→ https://ptgmedia.pearsoncmg.com/imprint_downloads/microsoftpress/bookreg/9780137383658_SourceFiles.zip
- Dino Esposito (11 books)
- Francesco Esposito (3 books)
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