Category Archives: Machine Learning

“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

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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

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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

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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

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Naive Bayes Classification Example Using Raw Python 3.7

I’m preparing the content for an all-day hands-on workshop. My main topics are all about neural networks, but I have a few classical techniques too, including naive Bayes classification. Here’s an example that I’ll use in the workshop. There are … Continue reading

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The Intuition Behind Naive Bayes Classification

I’m preparing an all-day hands-on workshop. My main topics are all about neural networks, but I have a few classical techniques too, including naive Bayes classification. I start my explanation of naive Bayes by explaining the general idea (“the intuition”). … Continue reading

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Preparing a Machine for the 2022 Predictive Analytics World All-Day Workshop

I will be presenting an all-day, hands-on machine learning workshop at the 2022 Predictive Analytics World (PAW) and Machine Learning Week (MLW) events, on Monday, June 20, in Las Vegas. See https://www.predictiveanalyticsworld.com/machinelearningweek/. The PAW / MLW events have lots of … Continue reading

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Installing Anaconda3 2020.02 with Python 3.7.6 on Windows 10/11

Anaconda is a collection of software packages that contains a base Python engine plus over 500 compatible Python packages. You need to install Anaconda Python before you can install the PyTorch or Keras/TensorFlow neural network code libraries. See the links … Continue reading

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Revisiting My Standard k-Means Clustering Example

I work at a very large tech company. One of my job responsibilities is to give training classes on machine learning topics. I focus on deep neural topics, especially PyTorch, but it’s my responsibility to address basic topics such as … Continue reading

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Weighted k-Nearest Neighbors Classification Example Using Python

One of the most fundamental machine learning classification techniques is k-nearest neighbors (k-NN). If you have a set of labeled data points, and you want to classify a new unlabeled data item, you compute the distance of the unlabeled item … Continue reading

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