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

Josh Patterson
Director, Field Engineering, Skymind

Website | @jpatanooga

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 of 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

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 (Skymind)
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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.
2:55pm3:35pm Thursday, September 28, 2017
Data science & advanced analytics, Machine Learning & Data Science
Location: 1A 12/14 Level: Intermediate
Secondary topics:  Deep learning, Streaming
Josh Patterson (Skymind), Kirit Basu (StreamSets )
Enterprises building data lakes often have to deal with very large volumes of image data that they have collected over the years. Josh Patterson and Kirit Basu explain how some of the most sophisticated big data deployments are using convolutional neural nets to automatically classify images and add rich context about the content of the image, in real time, while ingesting data at scale. Read more.