Dr. James McCaffrey of Microsoft Research provides a code-driven tutorial on PUL problems, which often occur with security or medical data in cases like training a machine learning model to predict if a hospital patient has a disease or not.
- By James McCaffrey
- 05/21/2021
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.
- By James McCaffrey
- 05/07/2021
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.
- By James McCaffrey
- 04/13/2021
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.
- By James McCaffrey
- 04/01/2021
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.
- By James McCaffrey
- 03/12/2021
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.
- By James McCaffrey
- 03/03/2021
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.
- By James McCaffrey
- 02/11/2021
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.
- By James McCaffrey
- 02/02/2021
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.
- By James McCaffrey
- 01/25/2021
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.
- By James McCaffrey
- 01/04/2021
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.
- By James McCaffrey
- 12/15/2020
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.
- By James McCaffrey
- 12/04/2020
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.
- By James McCaffrey
- 11/24/2020
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.
- By James McCaffrey
- 11/04/2020
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.
- By James McCaffrey
- 10/14/2020