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Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few females.
05/07/2021
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Microsoft dev teams for Azure Machine Learning (Azure ML) and Visual Studio Code have improved the developer experience in the super-popular, open source-based, cross-platform code editor.
05/05/2021
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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.
04/13/2021
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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.
04/01/2021
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Updates to Microsoft's AI/ML tooling highlight recent developments in the .NET dev world, which include PeachPie 1.0 (PHP in .NET) and new performance monitoring support for Xamarin.
03/18/2021
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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.
03/12/2021
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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.
03/03/2021
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Using Python in Visual Studio Code for machine learning model training and experimentation is easier in the February 2021 update to the tool that fosters Python programming in Microsoft's popular, open source, cross-platform code editor.
02/18/2021
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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.
02/11/2021
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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.
02/09/2021
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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.
02/02/2021
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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.
01/25/2021
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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.
01/04/2021
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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.
12/15/2020
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Dr. James McCaffrey of Microsoft Research kicks off a four-part series on multi-class classification, designed to predict a value that can be one of three or more possible discrete values.
12/04/2020
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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
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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
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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
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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
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Microsoft improved the object detection capabilities of its ML.NET machine learning framework for .NET developers, adding the ability to train custom models with Model Builder in Visual Studio.
09/28/2020
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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
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With the U.S. already adjusting to the new remote-work reality caused by COVID-19, remote learning is taking center stage as the school season starts, and Microsoft is touting its Azure Cognitive Services to help.
09/08/2020
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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
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"We have been working tirelessly to enable IntelliCode for more programming languages and, in the meantime, researching ways to improve the model precision and coverage to deliver an even more satisfying user experience."
08/24/2020
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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