Deep Learning with Cloudera

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Deep learning expands boundaries of the possible. Detecting fraud. Predicting claims. Diagnosing cancer. Deep learning solves these problems and many others.

However, organizations struggle to make deep learning work. Register for this deep learning webinar to learn:

  • Practical ways to move forward with deep learning
  • Deep learning without massive new investments
  • How to implement self-service deep learning on demand

Cloudera—with tools like the Cloudera Data Science Workbench—helps you bring deep learning to your data, for new insights and applications.

A demonstration of Cloudera Data Science Workbench is included in the webinar.

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On-Demand Webinar

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Add to calendar 2017-08-24 10:00:00 2017-08-24 11:00:00 America/Los_Angeles Deep Learning with Cloudera Deep learning expands boundaries of the possible. Detecting fraud. Predicting claims. Diagnosing cancer. Deep learning solves these problems and many others. This session includes a demonstration of Cloudera Data Science Workbench. Online Webinar






Featured speakers

Arun Krishnakumar

Engineer - Data Science Workbench
Cloudera

Arun is an engineer on the Cloudera Data Science Workbench team. He recently worked on enabling GPU support for the product. He has been working in the computer industry for about 15 years starting with low level programming in the Linux kernel and moving up the stack. Nowadays he usually works in user-space and codes for applications using Go, Java, Javascript and Python. Current Interests: deep learning, distributed computing.

Thomas Dinsmore

Director of Product Marketing for Cloudera Data Science

Thomas Dinsmore is the Director of Product Marketing for Data Science at Cloudera. Previously, as an independent consultant, he provided machine learning market insight to private clients seeking intelligence about the machine learning marketplace. Before launching his consultancy in 2015, he served as an analytics expert for The Boston Consulting Group; Director of Product Management for Revolution Analytics (Microsoft); Solution Architect for IBM Big Data, SAS and PriceWaterhouseCoopers. In a thirty-year career, he has led or contributed to analytic solutions for more than five hundred clients across vertical markets and around the world. Thomas holds an MBA in Accounting and Decision Sciences from the University of Pennsylvania - The Wharton School and a BA from Boston University.