SQL Server and SDS


Autoencoder Anomaly Detection Using PyTorch

Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like detecting credit card fraud.

How To: Create a Streaming Data Loader for PyTorch

When training data won't fit into machine memory, a streaming data loader using an internal memory buffer can help. Dr. James McCaffrey of Microsoft Research shows how.

Survey Finds Slow EF Core Adoption, Surprising Dev Team

Microsoft's data dev team recently shipped Entity Framework Core 6.0 Preview 2, which comes a couple months after a survey surprised them with indications many developers are sticking with tech that can be more than 10 years old.

Neural Regression Using PyTorch: Model Accuracy

Dr. James McCaffrey of Microsoft Research explains how to evaluate, save and use a trained regression model, used to predict a single numeric value such as the annual revenue of a new restaurant based on variables such as menu prices, number of tables, location and so on.

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Neural Regression Using PyTorch: Training

The goal of a regression problem is to predict a single numeric value, for example, predicting the annual revenue of a new restaurant based on variables such as menu prices, number of tables, location and so on.

What's New in EF Core 6.0 Preview 1

The Entity Framework Core 6 dev team shipped Preview 1 this week, headed toward a debut with the larger, unifying .NET 6 umbrella platform in November.

Neural Regression Using PyTorch: Defining a Network

Dr. James McCaffrey of Microsoft Research presents the second of four machine learning articles that detail a complete end-to-end production-quality example of neural regression using PyTorch.

No. 2 Data Science IDE? VS Code

VS Code's popularity in the data science camp was revealed in a survey by Kaggle, a Google subsidiary that fosters an online community of data scientists and machine learning practitioners.

Neural Regression Classification Using PyTorch: Preparing Data

Dr. James McCaffrey of Microsoft Research presents the first in a series of four machine learning articles that detail a complete end-to-end production-quality example of neural regression using PyTorch.

Multi-Class Classification Using PyTorch: Model Accuracy

Dr. James McCaffrey of Microsoft Research continues his four-part series on multi-class classification, designed to predict a value that can be one of three or more possible discrete values, by explaining model accuracy.

Entity Framework Core 6: What Developers Want

Microsoft outlined its plan for Entity Framework Core 6, which in November will take its place as the data access component of the landmark .NET 6, a long-term support (LTS) release that will mark Microsoft's transition from the Windows-only .NET Framework to an open source, cross-platform umbrella offering of all things .NET.

Azure SQL Database Ranked Among Top 3 Databases of 2020

Microsoft touted the inclusion of Azure SQL Database among the top three databases of 2020 in a popularity ranking by DB-Engines, which collects and manages information about database management systems, updating its lists monthly.

Multi-Class Classification Using PyTorch: Training

Dr. James McCaffrey of Microsoft Research continues his four-part series on multi-class classification, designed to predict a value that can be one of three or more possible discrete values, by explaining neural network training.

Multi-Class Classification Using PyTorch: Defining a Network

Dr. James McCaffrey of Microsoft Research explains how to define a network in installment No. 2 of his four-part series that will present a complete end-to-end production-quality example of multi-class classification using a PyTorch neural network.

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.

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