Description: An article about redefining risk management, written entirely by ChatGPT.

Description: Neural networks have fallen only to rise again twice. The latest incarnation borrows much from the past but adds elements to support deep learning. This article demonstrates the state of the art in deep neural network programming.

Description: A look into how machine learning models implement classification and regression at a deeper level. Covers topics such as multi-regression, softmax, and ROC charts.

Description: This article describes the history and in some respects, the future of Big Data, and the tools we can use to handle it for PM and for machine learning. One such tool is Vowpal Wabbit, which sounds like something from a Bugs Bunny and Elmer Fudd cartoon (“Dwat that wabbit”), but it really is a popular approach to process a dataset of any size, as there is no need to load all the data into memory. Big data is forcing us to enhance many of our tools. So is the increased actuarial usage of stochastic-in-stochastic analyses (nested stochastic processes), which can result in unacceptably long program run times. Many of our popular computer languages are not inherently well suited for parallel computations.

Description: Fuzzy logic is form of multi-valued logic that allows the creation of fuzzy rules. This article introduces the "sets" package for the R programming language for fuzzy logic.

Description: Python & Scikit-Learn are a powerful combination for predictive modeling. Using Scikit-Learn you gain access to a variety of models, such as support vector machines, decision trees, gradient boosting and others. Scikit-Learn is very popular with the Kaggle community.

Description: This article shows how to use a RBF Neural network to predict if a breast tumor is malignant. The network is trained using a genetic algorithm that uses C# multi-threading.

Description: Agent based modeling is a technique that can be used to predict complex interactions between individuals in an artificial environment. This article provides an introduction to Repast Py.

Description: An exploration of moving predictive modeling capabilities in R to real time operations.

Description: Evaluates the ability of various model types to automatically approximate various mathematical formulas. This can be used as a guide for feature engineering.