C#


How To Refactor for Dependency Injection, Part 2: Composition Root

Ondrej Balas continues his discussion on refactoring your code for dependency injection, this time focusing on the composition root pattern.

Deep Neural Networks: A Getting Started Tutorial

Deep Neural Networks are the more computationally powerful cousins to regular neural networks. Learn exactly what DNNs are and why they are the hottest topic in machine learning research.

Xamarin Support Added to Azure Mobile Services Offline

The upgrade is made possible by using SQLitePCL.

Virtual Reality in .NET, Part 3: 3D With Distortion and Head Tracking

Go deeper into the Oculus Rift SDK.

Visual Studio '14' CTP Available as Azure VM

MSDN subscribers can try it for free with their Microsoft-supplied credits.

Visual Studio '14' CTP Released

It's a preview of the next version of the IDE, expected to hit in 2015.

Adventures in Nepotism

What's proper etiquette for handling code snafus when working for -- literally -- a mom-and-pop company?

Xamarin 3 Unveiled

Tighter integration with Visual Studio among the highlights.

Understanding the Android Resource System

Learn how to leverage the powerful resource system of Android to support a variety of Android devices and languages.

Visual Studio 2013 Update 3 Gets its First CTP

The Update comes fewer than two weeks after the official release of Update 2 at TechEd North America.

How To Refactor for Dependency Injection, Part 1: Cleaning Up

Take control of your application's behavior and move toward dependency injection by refactoring your existing code.

Neural Network Dropout Training

Dropout training is a relatively new algorithm which appears to be highly effective for improving the quality of neural network predictions. It's not yet widely implemented in neural network API libraries. Learn how to use dropout training if it's available in an existing system, or add dropout training to systems where it's not yet available.

Using Knockout Custom Binding Handlers

Knockout custom binding handlers can help simplify integration with third-party JavaScript libraries. Here's how.

Neural Network Cross Entropy Error

To train a neural network you need some measure of error between computed outputs and the desired target outputs of the training data. The most common measure of error is called mean squared error. However, there are some research results that suggest using a different measure, called cross entropy error, is sometimes preferable to using mean squared error.

Telerik Makes Kendo UI Available as Open Source Platform

Kendo UI Core includes the entire mobile framework.

Microsoft Makes 'Roslyn' Compiler Open Source

Also announced was a new interop organization called The .NET Foundation.

Microsoft Promises C++ Power, C# Dev Efficiency With .NET Native

The developer preview was released yesterday.

Microsoft Takes Cross-Platform Development to Another Level

Release Candidate 2 of Visual Studio 2013 also announced.

Neural Network How-To: Code an Evolutionary Optimization Solution

Evolutionary optimization can be used to train a neural network. A virtual chromosome holds the neural network's weights and bias values, and the error term is the average of all errors between the network's computed outputs and the training data target outputs. Learn how to code the solution.

ASP.NET MVC Extensibility with MEF

How to use MEF to add validation rule components to an ASP.NET MVC Web application.

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