Description: Bayesian networks are one of many models that are useful for predictive modeling. This article introduces the concept of Bayesian network and shows how time-series data can be modeled for a Bayesian network.

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

Description: Jeff Sagarin is one of the most well-known figures in the sports analytics world, with his sports ratings appearing in USA Today for over 30 years. Few may know that he started his career in the actuarial world, even passing three exams before his sports ratings took off. This article explores the parallels between the challenges of developing sports rating systems and the challenges that actuaries face every day.

Description: An introduction to creating Shiny web applications, with sample code available to reproduce the interactive application displayed in screenshots in the article.

Description: The newer predictive modeling tools are powerful but they work best when supported by a lot of computing power. Parallel processing is very powerful but often very difficult to implement. Dihui and Richard take us through the maze of options with H2O, SparkNet/MLib and Mahout. Which do you use for a generalized linear model (GLM), and which is better for high-volume data manipulation?

Description: Introduction to PMML and how to use of it in the R programming language. PMML is an interchange format for predictive models.

Description: Here we break down deep learning into its various conceptual sets so as to have a better intuitive understanding of deep learning.

Description: Looks at free and inexpensive resources for the novice in predictive analytics, covering the data analysis theory as well as the most commonly used languages/software used in the field.

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