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Programming ML.NET

Programming ML.NET Computer Science

Programming ML.NET

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Description
The expert guide to creating production machine learning solutions with ML.NET! 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 Esposito’s best-selling Programming ASP.NET, this book takes the same scenario-based approach Microsoft’s team used to build ML.NET itself. After a foundational overview of ML.NET’s 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 user’s 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 learning–based 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.NET’s 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 aren’t enough- “Buy” neural networking via the Azure Cognitive Services API, or explore building your own with Keras and TensorFlow
Product details
Binding:
Paperback
Edition:
1
Number of Pages:
256
Release Date:
2022-04-14
Publication Date:
2022-05-30
Publisher:
Pearson Education (US)
Languages:
Original: English
ISBN10:
0137383657
ISBN13:
9780137383658
GPSR Manufacturer Reference:
Weight:
464 g
Height:
185 cm
Width:
228 cm
Thickness:
15 cm

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Almost no signs of wear. Book pages have no markings, accessories are intact and all other media are in good condition.
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