January 2019
Volume 34 Number 1
[Machine Learning]Introduction to PyTorch on WindowsPyTorch is a neural network library that is quite different from and operates at a lower level than popular libraries like Microsoft CNTK, Google TensorFlow, and scikit-learn. Learn how to get started with PyTorch library and leverage the additional control over code. |
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Connect(); 2018 Special Issue
The Connect(); conference is part of a larger conversation between Microsoft and its developer community. This special issue of MSDN Magazine explores the vision Microsoft has articulated and how it will impact developers. |
[.NET]Machine Learning Through Probabilistic ProgrammingProbabilistic programming is a useful approach to building the kinds of statistical models used to solve machine learning problems. Learn how Infer.NET, the Microsoft framework that provides algorithms for making probabilistic inferences from data, makes this easier to implement. |
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[Data Points]A Peek at the EF Core Cosmos DB Provider PreviewIn the first of a two-part series, Julie Lerman takes a look at the Cosmos DB provider preview in EF Core, discussing some of the basics, including getting the provider into your solution, letting EF Core create the Cosmos DB database, and storing data into a collection. |
[Machine Learning]Leveraging the Beliefs-Desires-Intentions Agent ArchitectureLearn about the Beliefs-Desires-Intentions (BDI) agent architecture, and see how to apply it to a real life scenario, such as implementing a Travel Assistant Agent (TAA) in C#. |
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[The Working Programmer]Coding NakedThe Naked Objects Framework aims to further the idea that developers should focus solely on the business domain while users should be able to work with the objects directly, without additional decoration. Ted Neward begins a new series that will examine the underlying concepts. |
[SQL]Introducing Azure SQL Database HyperscaleLearn about Azure SQL Database Hyperscale, a revolutionary new architecture that has the unique benefit of providing full compatibility with previous generations of SQL engines. |
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[Cutting Edge]Template-Based Components in BlazorAny reasonable flavors of components need to have support for templates, and Blazor components are no exception. In his latest column on Blazor, Dino Esposito explores how Blazor components work by updating the type-ahead example featured in his earlier columns, turning it into a template-based component. |
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[Test Run]Self-Organizing Maps Using C#Self-organizing maps are relatively simple machine learning techniques/objects, but they can be confusing, both because there are so many variations and because they have characteristics that resemble several other ML techniques. James McCaffrey explains how they work and how they can be useful. |
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[Don't Get Me Started]Mentoring AgainOne of the best ways to improve your own skills is to help someone else improve theirs, writes David Platt, as he explores the challenge of mentoring his daughter’s high school robotics team. |
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[Editor's Note]Advancing AIThe January issue of MSDN Magazine offers a focus on machine learning and artificial intelligence, and explores advancing toolsets like the PyTorch low-level neural network library and the .NET-friendly ML.NET framework. |