Description: This article explains how to create more maintainable models using abstractions by relating it to option, machine learning and machine intelligence models.

Description: Process of building a predictive model and related considerations.

Description: This paper describes techniques that can be used to access any specific piece of information in Wikipedia, such as medical information.

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: Summary of the first annual predictive analytics (PA) symposium specifically targeted to help actuaries get into PA & to take it to the next level of expertise.

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: Overview and review of books to teach yourself about deep learning.

Description: This actually is a copy of an appendix of a research paper sponsored by the SOA Committee on Finance Research. You can read the entire research paper: “Lapse Modeling for the Post-Level Period—a Practical Application of Predictive Modeling” at the SOA site https://www.soa.org/Research/Research-Projects/Finance-Investment/lapse-2015-modeling-post-level/#sthash.W9lERSls.dpbs, but you can read this valuable appendix copied here in this issue to see how to build a PM step-by-step using your data and the R programming language. We thank Richard Xu, Dihui Lai, Minyu Cao, Scott Rushing, and Tim Rozar for their excellent paper and the Society of Actuaries, for the permission to reprint this portion of the paper.

Description: This article describes how to model variable annuities using a GPU along with some of the stumbling blocks.

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.