Author Archives: jamesdmccaffrey

MNIST Image Classification Using Keras 2.8 on Windows 11

Posted on May 27, 2022 by jamesdmccaffrey

One of my standard neural network examples is image classification on the MNIST dataset. The full MNIST (modified National Institure of Standards and Technology) dataset has 60,000 images for training and 10,000 images for testing. Each image is a 28 … Continue reading →

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MNIST Image Classification Using PyTorch 1.10 on Windows 11

Posted on May 26, 2022 by jamesdmccaffrey

One of my standard neural network examples is image classification on the MNIST dataset. The full MNIST (modified National Institute of Standards and Technology) dataset has 60,000 images for training and 10,000 images for testing. Each image is a 28 … Continue reading →

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“Contrastive Loss Representation for Anomaly Detection Has Cybersecurity Implications” on the Pure AI Web Site

Posted on May 25, 2022 by jamesdmccaffrey

I contributed to an article titled “Contrastive Loss Representation for Anomaly Detection Has Cybersecurity Implications” in the May 2022 edition of the online Pure AI Web site. See https://pureai.com/articles/2022/05/03/anomaly-detection.aspx. The article describes a type of neural network architecture called contrastive … Continue reading →

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Regression (Employee Income) Using Keras 2.8 on Windows 11

Posted on May 24, 2022 by jamesdmccaffrey

One of my standard neural network examples is to predict employee income from sex, age, city, and job-type. Predicting a single numeric value is usually called a regression problem. (Note: “logistic regression” predicts a single numeric probability value between 0.0 … Continue reading →

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Regression (Employee Income) Using PyTorch 1.10 on Windows 11

Posted on May 23, 2022 by jamesdmccaffrey

One of my standard neural network examples is to predict employee income from sex, age, city, and job-type. Predicting a single numeric value is usually called a regression problem. (Note: “logistic regression” predicts a single numeric probability value between 0.0 … Continue reading →

Posted in PAW, PyTorch | 1 Comment

Understanding SimCLR – Simple Contrastive Loss Representation for Image Data

Posted on May 20, 2022 by jamesdmccaffrey

I’ve been looking at an interesting research paper titled “A Simple Framework for Contrastive Learning of Visual Representations” (2020) by T. Chen, S. Kornblith, M. Norouzi, and G. Hinton. The main idea is to take unlabeled image data and use … Continue reading →

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Autoencoder Anomaly Detection Using Keras 2.8 on Windows 11

Posted on May 19, 2022 by jamesdmccaffrey

Every few months I revisit my standard neural network examples to make sure that changes in the underlying code libraries (PyTorch, Keras/TensorFlow) haven’t introduced a breaking change(s). One of my standard examples is autoencoder anomaly detection. The idea is to … Continue reading →

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Logistic Regression for the Banknote Problem Using Raw Python

Posted on May 18, 2022 by jamesdmccaffrey

Every few months I implement a logistic regression (binary classification) model using raw Python (or some other language). The idea is that coding is a skill that must be practiced. One rainy Pacific Northwest afternoon, I zapped out logistic regression … Continue reading →

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Autoencoder Anomaly Detection Using PyTorch 1.10 on Windows 11

Posted on May 17, 2022 by jamesdmccaffrey

Every few months I revisit my standard neural network examples to make sure that changes in the underlying code libraries (PyTorch, Keras/TensorFlow) haven’t introduced a breaking change(s). One of my standard examples is autoencoder anomaly detection. The idea is to … Continue reading →

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Naive Bayes Classification Using C# in Visual Studio Magazine

Posted on May 16, 2022 by jamesdmccaffrey

I wrote an article titled “Naive Bayes Classification Using C#” in the May 2022 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2022/05/02/naive-bayes-classification-csharp.aspx. I present a complete demo program. The demo uses a set of 40 data items where each item … Continue reading →

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