Collaborative & Scalable Open Source Data Science: Deep Learning, Optimization & Education
Jonathan Entwistle from IBM present webinar, “Collaborative & Scalable Open Source Data Science: Deep Learning, Optimization & Education.”
Video Description
We will cover open source techniques written in languages like R, Python, or Spark for the purposes of implementing a couple of health care use cases like image recognition, scalable genomics, and IBM's approach to resource optimization. For example, one of the primary use cases of IBM's Watson for Oncology is to have Watson automatically identify cancer in X-Rays and other unstructured data. We'll be using a simple data set to recreate the same approaches as IBM Watson using pure open source technology. The session will include available sample data, a cloud based platform in which users can leverage the same environment and follow along, as well as develop new use cases on their own for free.
Jonathan would like to hear from you if you have questions regarding the lecture and would be open to helping anyone who is interested in using the technology used in this presentation. For Jonathan’s contact information, please view presentation slides below or view the beginning of this lecture.
For more information about Jonathan visit: http://www.ibmbigdatahub.com/blog/aut...
View slides from this lecture:
https://drive.google.com/open?id=0B4I...
About the Speaker
Jonathan Entwistle has been working in the federal government data science community for more than 10 years. He has worked with large programs within NIH, FDA, and CMS, as well as a masters’ degree in Predictive Analytics from Northwestern University. He specializes in data science at scale leveraging technologies like Hadoop and Spark. He lives with his family in Maryland and routinely discusses data science at dinner with his statistician wife and 2 year old son.
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