Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY
Dave Kale

Dave Kale
Deep Learning Engineer, Skymind


David Kale is a deep learning engineer at Skymind and a PhD candidate in computer science at the University of Southern California, where he is advised by Greg Ver Steeg of the USC Information Sciences Institute. His research uses machine learning to extract insights from digital data in high-impact domains, such as healthcare, and he collaborates with researchers from Stanford Center for Biomedical Informatics Research and the YerevaNN Research Lab. Recently, David pioneered the application of deep learning to modern electronic health records data. At Skymind, he works with clients and partners to develop and deploy deep learning solutions for real world problems. David co-organizes the Machine Learning for Healthcare Conference (MLHC) and has served as a judge in several XPRIZE competitions, including the upcoming IBM Watson AI XPRIZE. He is the recipient of the Alfred E. Mann Innovation in Engineering Fellowship.


1:30pm5:00pm Tuesday, September 26, 2017
Artificial Intelligence
Location: 1A 12/14 Level: Intermediate
Secondary topics:  Deep learning, Healthcare
Josh Patterson (Skymind), Vartika Singh (Cloudera), Dave Kale (Skymind), Tom Hanlon (Functional Media)
Average rating: **...
(2.00, 1 rating)
Josh Patterson, Vartika Singh, David Kale, and Tom Hanlon walk you through interactively developing and training deep neural networks to analyze digital health data using the Cloudera Workbench and Deeplearning4j (DL4J). You'll learn how to use the Workbench to rapidly explore real-world clinical data, build data-preparation pipelines, and launch training of neural networks. Read more.