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Category Archives: Machine Learning
A Quick Demo of the DBSCAN Clustering Algorithm
I was reading a research paper this morning and the paper used the DBSCAN (“densitybased spatial clustering of applications with noise”) clustering algorithm. DBSCAN is somewhat similar to kmeans clustering. Both work only with strictly numeric data. In kmeans you … Continue reading
Posted in Machine Learning
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Differential Evolution Optimization in Visual Studio Magazine
I wrote an article titled “Differential Evolution Optimization” in the September 2021 edition of the Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2021/09/07/differentialevolutionoptimization.aspx. The most common type of optimization for neural network training is some form of stochastic gradient descent (SGD). SGD … Continue reading
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Example of Computing KullbackLeibler Divergence for Continuous Distributions
In this post, I present an example of estimating the KullbackLeibler (KL) divergence between two continuous distributions using the Monte Carlo technique. Whoa! Just stating the problem has a massive amount of information. The KL divergence is the key part … Continue reading
Posted in Machine Learning, PyTorch
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The Wasserstein Distance Using C#
The Wasserstein distance has many different variations. In its simplest form the Wasserstein distance function measures the distance between two discrete probability distributions For example, if: double[] P = new double[] { 0.6, 0.1, 0.1, 0.1, 0.1 }; double[] Q1 … Continue reading
Posted in Machine Learning
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Another Set of Beautiful Machine Learning Visualizations from Thorsten Kleppe
Thorsten Kleppe is a fellow machine learning enthusiast who creates beautiful ML visualizations. Thorsten sent me some of his latest work. Thorsten’s new visualizations are based on a logistic regression model applied to the MNIST dataset. The MNIST dataset contains … Continue reading
Posted in Machine Learning
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Wasserstein Distance Using C# and Python in Visual Studio Magazine
I wrote an article titled “Wasserstein Distance Using C# and Python” in the August 2021 edition of Microsoft Visual Studio Magazine. See https://visualstudiomagazine.com/articles/2021/08/16/wassersteindistance.aspx. There are many different ways to measure the distance between two probability distributions. Some of the most … Continue reading
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Comparing Wasserstein Distance with KullbackLeibler Distance
There are many ways to calculate the distance between two probability distributions. Four of the most common are KullbackLeibler (KL), JensenShannon (JS), Hellinger (H), and Wasserstein (W). When I was in school, I learned that W was superior to KL, … Continue reading
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Preparing the Boston Housing Dataset for PyTorch
The Boston Housing dataset is a standard benchmark for regression algorithms. The goal of the Boston Housing problem is to predict the median price of a house in one of 506 towns near Boston. The data has 14 columns. There … Continue reading
Posted in Machine Learning
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Researchers Explore Differential Evolution Optimization for Machine Learning on Pure AI
I contributed to an article titled “Researchers Explore Differential Evolution Optimization for Machine Learning” posted on the Pure AI web site. See https://pureai.com/articles/2021/08/02/differentialevolutionoptimization.aspx. The article explains what differential evolution optimization (DEO) is, and describes how researchers at Microsoft demonstrated that … Continue reading
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Why I Don’t Use MinMax or ZScore Normalization For Neural Networks
Normalization is the process of scaling numeric predictor values so that they’re all roughly in the same range, typically 0.0 to 1.0 (minmax normalization) or about 4.0 to +4.0 (zscore normalization). Over the past few years I have become quite … Continue reading
Posted in Machine Learning
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