Tips & How-To Articles, Tutorials

  • Binary Classification Using PyTorch: Model Accuracy

    In the final article of a four-part series on binary classification using PyTorch, Dr. James McCaffrey of Microsoft Research shows how to evaluate the accuracy of a trained model, save a model to file, and use a model to make predictions. 11/24/2020

  • Binary Classification Using PyTorch: Training

    Dr. James McCaffrey of Microsoft Research continues his examination of creating a PyTorch neural network binary classifier through six steps, here addressing step No. 4: training the network. 11/04/2020

  • Binary Classification Using PyTorch: Defining a Network

    Dr. James McCaffrey of Microsoft Research tackles how to define a network in the second of a series of four articles that present a complete end-to-end production-quality example of binary classification using a PyTorch neural network, including a full Python code sample and data files. 10/14/2020

  • Binary Classification Using PyTorch: Preparing Data

    Dr. James McCaffrey of Microsoft Research kicks off a series of four articles that present a complete end-to-end production-quality example of binary classification using a PyTorch neural network, including a full Python code sample and data files. 10/05/2020

  • How to Create and Use a PyTorch DataLoader

    Dr. James McCaffrey of Microsoft Research provides a full code sample and screenshots to explain how to create and use PyTorch Dataset and DataLoader objects, used to serve up training or test data in order to train a PyTorch neural network. 09/10/2020

  • Working with Local Storage in a Blazor Progressive Web App

    Thanks to Chris Sainty and Remi Bourgarel, working with local storage from a Blazor application running either in the browser or out of it is relatively easy. Testing your code can be equally easy but only if you set up support the real world of network connections. 09/09/2020

  • Data Prep for Machine Learning: Splitting

    Dr. James McCaffrey of Microsoft Research explains how to programmatically split a file of data into a training file and a test file, for use in a machine learning neural network for scenarios like predicting voting behavior from a file containing data about people such as sex, age, income and so on. 09/01/2020

  • Firm Automates Converting Visual Basic Apps to .NET Core

    Mobilize.Net, an "automated modernization" specialist headed by a former Microsoft corporate VP, has upgraded its Visual Basic upgrade tool to target .NET Core, the open source, cross-platform successor of the Windows-only .NET Framework. 08/28/2020

  • Data Prep for Machine Learning: Encoding

    Dr. James McCaffrey of Microsoft Research uses a full code program and screenshots to explain how to programmatically encode categorical data for use with a machine learning prediction model such as a neural network classification or regression system. 08/12/2020

  • Data Prep for Machine Learning: Normalization

    Dr. James McCaffrey of Microsoft Research uses a full code sample and screenshots to show how to programmatically normalize numeric data for use in a machine learning system such as a deep neural network classifier or clustering algorithm. 08/04/2020

  • Creating a Progressive Web App with Blazor WebAssembly

    Not surprisingly, it's dead easy to create an app in Blazor that runs outside of the browser window and (potentially) in an offline mode. Before you get carried away, though, there are some key design decisions to make. 08/03/2020

  • GitLab Takes Over VS Code Extension, Plans Improvements

    DevOps specialist GitLab has officially taken over the control of a GitLab extension for Microsoft's open source, cross-platform Visual Studio Code editor. 07/31/2020

  • Data Prep for Machine Learning: Outliers

    After previously detailing how to examine data files and how to identify and deal with missing data, Dr. James McCaffrey of Microsoft Research now uses a full code sample and step-by-step directions to deal with outlier data 07/14/2020

  • Mads Kristensen Unveils 'The Essentials' Visual Studio Extension Pack for All Devs

    Visual Studio senior program manager Mads Kristensen has created a new extension pack for the IDE to ease the acquisition of the basic tools that would benefit all developers. 07/09/2020

  • Data Prep for Machine Learning: Missing Data

    Turning his attention to the extremely time-consuming task of machine learning data preparation, Dr. James McCaffrey of Microsoft Research explains how to examine data files and how to identify and deal with missing data. 07/06/2020

  • Write Once, Run Everywhere with .NET and the Uno Platform

    Right now, in Visual Studio, you can create a solution that takes a single UI with its code and shares it across Windows, Android, macOS, iOS and web browsers. It's not a perfect cross-platform solution (yet), but it's here now. 06/30/2020

  • Working With PyTorch Tensors

    Dr. James McCaffrey of Microsoft Research presents the fundamental concepts of tensors necessary to establish a solid foundation for learning how to create PyTorch neural networks, based on his teaching many PyTorch training classes at work. 06/15/2020

  • Getting Started with PyTorch 1.5 on Windows

    Dr. James McCaffrey of Microsoft Research uses a complete demo program, samples and screenshots to explains how to install the Python language and the PyTorch library on Windows, and how to create and run a minimal, but complete, neural network classifier. 06/08/2020

  • Clustering Non-Numeric Data Using C#

    Clustering non-numeric -- or categorial -- data is surprisingly difficult, but it's explained here by resident data scientist Dr. James McCaffrey of Microsoft Research, who provides all the code you need for a complete system using an algorithm based on a metric called category utility (CU), a measure how much information you gain by clustering. 06/03/2020

  • The End of Integration Testing: If You've Passed All the Tests ...

    Really, you only need to do two kinds of testing: Unit testing (to make sure that your individual components work) and end-to-end testing (to make sure your application works). Anything else is just a waste of your time. 05/07/2020

  • Data Clustering with K-Means++ Using C#

    Dr. James McCaffrey of Microsoft Research explains the k-means++ technique for data clustering, the process of grouping data items so that similar items are in the same cluster, for human examination to see if any interesting patterns have emerged or for software systems such as anomaly detection. 05/06/2020

  • Creating Flexible Queries with Parameters in GraphQL

    GraphQL gives clients who call your Web services the ability to specify what properties of your data objects they want. Here are two ways to let those clients also specify which data objects they want. 05/05/2020

  • How to Do Kernel Logistic Regression Using C#

    Dr. James McCaffrey of Microsoft Research uses code samples, a full C# program and screenshots to detail the ins and outs of kernal logistic regression, a machine learning technique that extends regular logistic regression -- used for binary classification -- to deal with data that is not linearly separable. 04/29/2020

  • Top 3 Blazor Extensions for Visual Studio Code

    Some developers prefer to create applications with Microsoft's open-source Blazor tooling from within the open-source, cross-platform Visual Studio Code editor. Here are the top tools in the VS Code Marketplace for those folk, as measured by the number of installations. 04/08/2020

  • How to Invert a Machine Learning Matrix Using C#

    VSM Senior Technical Editor Dr. James McCaffrey, of Microsoft Research, explains why inverting a matrix -- one of the more common tasks in data science and machine learning -- is difficult and presents code that you can use as-is, or as a starting point for custom matrix inversion scenarios. 04/07/2020

Upcoming Events