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C# Today and Tomorrow
Mads Torgersen discusses how C# is evolving, how the teams work in the open source space, and some of the future features and changes to the language (C# 7).
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Performance and How to Measure It
Matt Warren takes a look at how to measure, what to measure and how get the best performance from .NET code, considering examples from the Roslyn codebase and StackOverflow (the product).
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Vowpal Wabbit, A Machine Learning System
John Langford discusses how to use Vowpal Wabbit in and as a machine learning system including architecture, unique capabilities, and applications, applied to personalized news recommendation.
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Being Meta
Jevgenij Nekrasov discusses doing meta-programming in .NET, including writing a custom DSL.
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Predicting the Future: Surprising Revelations trom Truly Big Data
Pushpraj Shukla discusses how Microsoft Bing predicts the future based on aggregate human behavior using one of the largest scale data sets, and recent progress in large scale deep learnt models.
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Integrating Different IDEs with a Common Set of Developer Services
David Staheli discusses approaches Microsoft is taking to plugin development, sharing experiences in reusing code across plugins for different IDEs, with demos of plugins in Eclipse, IntelliJ, and VS.
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Unfrying Your Brain with F#
Andrea Magnorsky discusses active patterns, computation expressions, parsers, using type providers and more. These language features help make code simpler and easier to maintain.
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A Board Game Night with Geeks
Felienne Hermans explains how she used F# to determine if the game Quarto can end up in a tie or if there is always a winner. The technique used can be applied to scheduling and register allocation.
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Creating a Rainstorm Using Infrared and C#
Lisa Taylor shares the story of programming trial and error. Using C#, JavaScript, pixels and bitmaps, loops and infrared light she created a digital rainstorm inside a shipping container.
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Beyond Lists
Phillip Trelford shows through live demos data structures that are orders of magnitude more performant than lists.
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NET Machine Learning: F# and Accord.NET
Alena Hall presents various machine learning algorithms available in Accord.NET - a framework for machine learning and scientific computing in .NET.
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Lessons in Extreme .NET Performance
Ben Watson provides a deep-dive introduction to what you need to know to squeeze out the ultimate performance from your .NET code, along with war stories from building the Bing platform query engine.
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