The Data Science Lab


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.

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.

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.

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.

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.

Multi-Class Classification Using PyTorch: Preparing Data

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.

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.

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.

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.

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.

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.