Artificial Intelligence Conference San Francisco 78/114

Artificial Intelligence Conference San Francisco 78/114

By
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Josh Patterson 
Director of Field Engineering, Skymind

Josh Patterson is the director of field engineering for Skymind. Previously, Josh ran a big data consultancy, worked as a principal solutions architect at Cloudera, and was an engineer at the Tennessee Valley Authority, where he was responsible for bringing Hadoop into the smart grid during his involvement in the openPDC project. Josh is a cofounder of the DL4J open source deep learning project and is a coauthor on the upcoming O’Reilly title Deep Learning: A Practitioner’s Approach. Josh has over 15 years’ experience in software development and continues to contribute to projects such as DL4J, Canova, Apache Mahout, Metronome, IterativeReduce, openPDC, and JMotif. Josh holds a master’s degree in computer science from the University of Tennessee at Chattanooga, where he did research in mesh networks and social insect swarm algorithms.

Sessions

9:00am – 5:00pm Sunday, September 17 & Monday, September 18
Neural networks for time series analysis using Deeplearning4j
Location: Franciscan C
Josh Patterson (Skymind), Susan Eraly (Skymind), Dave Kale (Skymind), Tom Hanlon (Skymind)
Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson, Susan Eraly, Dave Kale, and Tom Hanlon demonstrate how to use Deeplearning4j to build recurrent neural networks for time series data.