NWA Data Science Meetup
Bentonville, AR
Tuesday, April 5, 2016
The NWA Data Science Meetup is a group of data scientists, software engineers, and business analysts passionate in applying advances in machine learning, predicative analytics, and natural language processing to solving *real world* problems. As yak shavers, they are technology and language agnostic, embracing whatever it takes to get the job done.


Everything you always wanted to know about Deep Learning

Joseph Blue View Bio

According to rumor & innuendo, deep learning is the hottest thing to come out of data science since the first fair coin was struck in Asia Minor. The goals of this high-to-medium-level discussion are to de-mystify deep learning and help machine learning enthusiasts understand how it works, what it can do (and what it cannot), where to get it and what the future might hold. During this workshop, we will expose how deep learning evolved from neural networks, walk through the architecture and training of convolutional neural networks (CNN) and review practical examples of real-life use cases from the field. We will also touch on current developments (both advancements and challenges) and speculate on how businesses are beginning to adopt these models. Note: experience with Neural Networks isn’t a prerequisite for participation in this discussion, but we assume the attendees are aware of model-building essentials (e.g supervised vs. unsupervised, major categories of algorithms, overtraining, etc.).


Joseph Blue

In his role as Data Scientist at MapR, Joe assists customers in solving their big data problems, making efficient use of the Hadoop ecosystem to generate tangible results. Recent projects include debit card fraud & breach detection, lead generation from social data, customer matching through record linkage, lookalike modeling using browser history and real-time product recommendations.

Prior to MapR, Joe was the Chief Scientist for Optum (a division of UnitedHealth) and the principal innovator in analytics for healthcare. As a Sr. Fellow with OptumLabs, he applied machine learning concepts to healthcare issues such as disease prediction from co-morbidities, estimation of PMPY (member cost), physician scoring and treatment pathways. As a leader in the Payment Integrity business, he built anomaly detection engines responsible for saving $100M annually in claim overpayments.