Advanced Machine Learning .NET Applications

سرفصل های دوره

Take your machine learning .NET applications to the next level in this course, with software engineer Sam Nasr. Join Sam as he explores using ML.Net with other frameworks like ONNX and TensorFlow. In addition, he discusses best practices for properly collecting data for ML, source control, versioning, and common pitfalls.

01 - Introduction

01 - Advanced machine learning .NET

02 - What you should know

02 - 1. Preparing Data for Machine Learning

01 - Collecting data correctly

02 - Utilize SME

03 - Data types and structure

04 - Business logic

05 - Outliers

06 - Biases

07 - Data cleansing tools

08 - Demo Checking data

03 - 2. How to Generate an ONNX Model

01 - What is ONNX

02 - Set up the .NET project for ONNX

03 - Create a model

04 - Generate an ONNX model

05 - Using Netron

06 - Demo Generate an ONNX model in Visual Studio

04 - 3. How to Utilize TensorFlow Framework

01 - Image recognition vs. categorization

02 - What is TensorFlow

03 - Set up the .NET project for TensorFlow

04 - Train the model

05 - Evaluate the model

06 - Demo Train a TensorFlow model in Visual Studio

05 - 4. Model Maintenance

01 - What is MLOps

02 - Retraining the model

03 - Versioning

04 - Source control

06 - 5. Common Pitfalls

01 - Machine Learning Model not in the context menu

02 - Is 32-bit supported on Windows

03 - Ensure the app is targeting x64 or x86

04 - New project with a different build target

05 - Challenge Training and comparing ML.NET models

06 - Solution Training and comparing ML.NET models

07 - Conclusion

01 - Next steps

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تولید کننده: LinkedIn Learning (Lynda)