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Author Archives: jamesdmccaffrey
MNIST Image Classification Using Keras 2.8 on Windows 11
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
Posted in Keras, PAW
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MNIST Image Classification Using PyTorch 1.10 on Windows 11
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
Posted in PAW, PyTorch
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“Contrastive Loss Representation for Anomaly Detection Has Cybersecurity Implications” on the Pure AI Web Site
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
Posted in Machine Learning
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Regression (Employee Income) Using Keras 2.8 on Windows 11
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 Keras, PAW
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Regression (Employee Income) Using PyTorch 1.10 on Windows 11
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
Understanding SimCLR – Simple Contrastive Loss Representation for Image Data
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
Posted in Machine Learning
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Autoencoder Anomaly Detection Using Keras 2.8 on Windows 11
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
Posted in Keras, PAW
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Logistic Regression for the Banknote Problem Using Raw Python
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
Posted in Machine Learning, PAW
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Autoencoder Anomaly Detection Using PyTorch 1.10 on Windows 11
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
Posted in PAW, PyTorch
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Naive Bayes Classification Using C# in Visual Studio Magazine
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
Posted in Machine Learning
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