Everything  you always wanted to know about Deep Learning - Data Science Phoenix - AZ - April 11, 2017 - Slides now available!

Document created by aalvarez on Mar 29, 2017Last modified by aalvarez on Apr 11, 2017
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Date:April 11, 2017
Location:ASU University Service Building

1551 S. Rural Rd., Tempe, AZ

Time:18:30 - 20:30
Registration Link:Everything you always wanted to know about Deep Learning - Data Science Phoenix (Phoenix, AZ) - Meetup 



• 6:30-6:45pm: Sign-in & network (with Food & Beverages by MapR)
• 6:45-7:00pm: Introduction & announcements
• 7:00-8:15pm: Presentation by Joseph Blue


Everything you always wanted to know about Deep Learning (but were afraid to ask) by Joe Blue

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.).


• 8:15-8:30pm: Q&A & wrap-up



Joe 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.

Tim Duquette

Tim Duquette is a Philadelphia-based Data Scientist on MapR's Professional Services team. As a Data Scientist, he works with customers in leveraging the Hadoop ecosystem to improve their workflows and discover new insights in their data. His most recent projects include improving targeting for autodialers and automating cluster setups with Ansible.

Prior to MapR, Tim was a Data Scientist at Teradata, where he built the data science piece of a churn modeling application for a major Canadian telecom. Current interests include machine learning on Hadoop, automating Data Science with Ansible, and discretizing development environments with Docker.




We appreciate Nicholas Frederick, Strategic Account Director, from MapR Technologies for sponsoring food and refreshments for this event.

Parking Options:
- Free covered parking - after 5:30 PM!! Address is 1551 S. Rural Rd., Tempe, AZ



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