Category Archives: PyTorch

I wrote an article titled “Logistic Regression Using PyTorch with L-BFGS” in the June 2021 edition of the online Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2021/06/23/logistic-regression-pytorch.aspx. Logistic regression is one of many machine learning techniques for binary classification — predicting one … Continue reading →

In computer science, and life, it helps to be smart but it’s also important to have determination. I’m not the smartest guy in the Universe, but once a problem gets stuck in my head it will stay there until it … Continue reading →

When creating a deep neural network, writing code to prepare the training data and serve it up in batches to the network is almost always difficult and time consuming. A regular PyTorch DataLoader works great for tabular style data where … Continue reading →

I wrote an article titled “Generating Synthetic Data Using a Generative Adversarial Network (GAN) with PyTorch” in the June 2021 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2021/06/02/gan-pytorch.aspx. A generative adversarial network (GAN) is a deep neural system that can … Continue reading →

Suppose you want to make a logistic regression binary classification model, for example to predict if a hospital patient is male or female based on variables such as age and hospitalization history. And suppose your training data file is very … Continue reading →

Even the simplest natural language processing problem is extremely difficult. With a basic neural network classifier, you usually have to normalize numeric data (such as dividing a person’s age by 100) and encode non-numeric data (e.g., “red” = 1,0,0) but … Continue reading →

A generative adversarial network (GAN) is a deep neural system that is designed to generate fake/synthetic data items. A GAN has a clever architecture made of two neural networks: a generator that creates fake data items, and a discriminator that … Continue reading →