dotnet

A call to the .NET OSS community to showcase projects through video at BUILD 2016. Outlines the PSEC framework (Problem, Solution, Example, Conclusion) for creating compelling technical presentations that humanize software.

Exploring how thinking of functions as data types can transform your approach to software design. Demonstrates delegates, lambda expressions, and practical applications in matrix operations that lead to more concise, readable code.

Deep dive into nuML's Descriptor pattern that bridges object-oriented class structures with mathematical ML algorithms. Includes both attribute-based markup and fluent interface approaches for late-binding scenarios.

Presenting the nuML machine learning library at CodeMash with a focus on accessibility and ease of use. Addresses predictive challenges, performance considerations, and provides talk materials for both supervised and unsupervised learning.

Breaking down supervised learning into binary classification, multi-class classification, and regression using .NET data types. Shows how the Perceptron algorithm can learn from labeled examples and includes model serialization for reuse.

Introducing machine learning to .NET developers by removing the math barrier with an intuitive attribute-based approach. Demonstrates automatic feature selection using simple class decorators, making ML algorithms accessible without deep statistical knowledge.