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
CEO, Patterson Consulting

Website | @jpatanooga

Josh Patterson is CEO of Patterson Consulting, a solution integrator at the intersection of big data and applied machine learning. In this role, he brings his unique perspective blending a decade of big data experience and wide-ranging deep learning experience to Fortune 500 projects. At the Tennessee Valley Authority (TVA), Josh drove the integration of Apache Hadoop for large-scale data storage and processing of smart grid phasor measurement unit (PMU) data. Post-TVA, Josh was a principal solutions architect for a young Hadoop startup named Cloudera (CLDR), as employee 34. After leaving Cloudera, Josh co-founded the Deeplearning4j project and co-wrote Deep Learning: A Practitioner’s Approach (O’Reilly Media). Josh was also the VP of Field Engineering for Skymind. Josh also co-wrote the upcoming Oreilly book “Kubeflow Operations”

Sessions

1:30pm5:00pm Tuesday, September 26, 2017
Artificial Intelligence
Location: 1A 12/14 Level: Intermediate
Secondary topics:  Deep learning, Healthcare
Josh Patterson (Patterson Consulting), Vartika Singh (Cloudera), Dave Kale (Skymind), Tom Hanlon (Functional Media)
Average rating: **...
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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 (Patterson Consulting), 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.