Books (By Me!)
Events (I Speak At!)
2021 G2E Conference
2021 DevIntersection Conference
2022 Visual Studio Live Conference
2021 Azure AI Conference
2021 Fall MLADS Conference
2021 Money 20/20 Conference
2021 TDWI Conference
2021 DEFCON Conference
2021 National Homeland Security Conference
2021 ISC West Conference
2021 ConnectIT Conference
Author Archives: jamesdmccaffrey
A Quick Demo of the DBSCAN Clustering Algorithm
I was reading a research paper this morning and the paper used the DBSCAN (“density-based spatial clustering of applications with noise”) clustering algorithm. DBSCAN is somewhat similar to k-means clustering. Both work only with strictly numeric data. In k-means 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/differential-evolution-optimization.aspx. The most common type of optimization for neural network training is some form of stochastic gradient descent (SGD). SGD … Continue reading
Posted in Machine Learning
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Yet Another MNIST Example Using Keras
It’s a major challenge to keep up with the continuous changes to the Keras/TensorFlow neural code library (and the PyTorch library too). I recently upgraded my Keras installation to version 2.6 and so I’m going through all my standard examples … Continue reading
Posted in Keras
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NFL 2021 Week 2 Predictions – Zoltar Likes Eight Vegas Underdogs
Zoltar is my NFL football prediction computer program. It uses reinforcement learning and a neural network. Here are Zoltar’s predictions for week #2 of the 2021 season. These predictions are tentative, in the sense that it usually takes Zoltar about … Continue reading
Determining If Two Sentences Are Paraphrases Of Each Other Using Hugging Face
Deep neural systems based on Transformer Architecture (TA) have revolutionized the field of natural language processing (NLP). Unfortunately, TA systems are insanely complex, meaning that implementing a TA system from scratch is not feasible, and implementing TA using a low-level … Continue reading
Posted in PyTorch
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A Simplified Approach for Ordinal Classification
In a standard classification problem, the goal is to predict a class label. For example, in the Iris Dataset problem, the goal is to predict a species of flower: 0 = “setosa”, 1 = “versicolor”, 2 = “virginica”. Here the … Continue reading
Posted in PyTorch
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Example of Computing Kullback-Leibler Divergence for Continuous Distributions
In this post, I present an example of estimating the Kullback-Leibler (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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NFL 2021 Week 1 Predictions – Zoltar Likes Six Underdogs
Zoltar is my NFL football prediction computer program. It uses reinforcement learning and a neural network. Here are Zoltar’s predictions for week #1 of the 2021 season. These predictions are tentative, in the sense that it usually takes Zoltar about … Continue reading
Posted in Zoltar
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A Recap of the 2021 National Homeland Security Conference
I gave a short talk at the 2021 National Homeland Security Conference. See https://www.nationalhomelandsecurity.org/. The event was held from August 30 through September 2. The event Web site states, “The National Homeland Security Conference brings together professionals in Homeland Security, … Continue reading
Posted in Conferences
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.NET Test Automation Recipes
Software Testing
SciPy Programming Succinctly
Keras Succinctly
R Programming
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