Adapting to Machine Learning
Geoffrey Hulten’s feature on machine learning patterns and practices addresses an important flank for developers increasingly engaged with machine learning and AI.
EF Core in a Docker Containerized App
Given the time she’s spent learning about EF Core and Docker, Julie Lerman thought it wouldn’t be too difficult to use them together to build a containerized app and add a data persistence mechanism. It turned out to be harder than she thought. Learn how and why, and use her experiences to ease your pain.
How Do Neural Networks Learn?
Backpropagation, gradient descent, and loss are all terms associated with machine learning. This article explores the process of backpropagation in depth and the process by which machines can learn using Keras.
What’s New in Xamarin.Forms 4.0
Xamarin.Forms 4.0 is still in preview, but a number of new features and improvements are already available, such as the new CollectionView and the updated CarouselView controls, the Visual property, and the Shell. Learn about what you’ll find in the latest version, and what kind of support you’ll see in the next edition of Visual Studio.
Closed-Loop Intelligence: A Design Pattern for Machine Learning
Machine learning is a fantastic tool, but getting the most out of it requires a lot more than building a model and making a few predictions. This article discusses the technology you can implement to support a machine learning based solution’s growth process, taking it from an error prone first release to a world class intelligence.
Secure Multi-Party Machine Learning with Azure Confidential Computing
Learn how sensitive data is stored in a SQL Server database with Always Encrypted with Secure Enclaves technology, and shared with a machine learning service that runs in a Trusted Execution Environment, using the Open Enclave SDK. In this example, a machine learning application enables multiple health care parties to share data to build a better predictive model.
Implementing Your Own Enterprise Search
Search is one of the most powerful functionalities in an IT infrastructure. Yet it remains widely misunderstood, abd noticed only when something is missing or broken. Learn how to get started developing an enterprise search API in C# with Solr and SolrNet.
Neural Anomaly Detection Using PyTorch
Anomaly detection is the process of finding rare items in a dataset. Using the PyTorch library, James McCaffrey presents a demo program that creates a neural autoencoder to handle anomaly detection, which comes with an addtional benefit in that neural techniques can handle non-numeric data by encoding that data.
A Laughing Matter
In the spirit of April Fool’s Day, David Platt decides to ask Cortana, Siri, Alexa and OK Google to tell him a joke. The resulting groaners prodded him to look more deeply into the uneasy relationship between computers and humor.
Ted Neward's ongoing exploration of the Naked Objects pattern offers a glimpse into object-oriented programming the way it was meant to be.