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

Description: A description of how to use Deep Learning and TensorFlow

Description: This article takes the reader on a speculative trip to the not too distant future to explore a hypothetical new life insurance company that has completely reinvented the way it does business, and explores some of the potential benefits to consumers and the company’s stakeholders.

Description: We actuaries are aware that soft skills are important but somehow, they appear distant and difficult to sustain.

Description: In this article, we are going to discuss the AR model by making connections to time-dependent ordinary differential equations.

Description: Summary of what the section has accomplished over the past year.

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: Ruminations on changing skill sets and professions, with a focus on actuaries and data scientists

Description: Contrary to what you might infer from the article title, the author provides counterpoint from some data science skeptics and suggests Big Data sometimes leads to sloppy analysis.