Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Deep learning: Assessing analytics project feasibility and computational requirements

14:5515:35 Wednesday, 24 May 2017
Location: Capital Suite 2/3
Average rating: **...
(2.67, 3 ratings)

What you'll learn

  • Explore the types of analytical problems that can be solved using AI
  • Understand the key tools you need to initiate an analytical AI project


The ability to transport and store energy has had a profound impact on the human race. Artificial intelligence is starting to have a similar effect. No wonder experts like Andre Ng have recently compared deep learning and artificial intelligence to electricity.

Adam Grzywaczewski offers an overview of the types of analytical problems that can be solved using AI and shares a set of heuristics that can be used to evaluate the feasibility of analytical AI projects. Adam then covers the computational profile of the deep learning workload and the infrastructure components that need to be set in place to fuel the successful deep learning training process, leaving you with the key tools you need to initiate an analytical AI project.

This session is sponsored by NVIDIA.

Photo of Adam Grzywaczewski

Adam Grzywaczewski


Adam Grzywaczewski is a deep learning solution architect at NVIDIA, where his primary responsibility is to support a wide range of customers in delivery of their deep learning solutions. Adam is an applied research scientist specializing in machine learning with a background in deep learning and system architecture. Previously, he was responsible for building up the UK government’s machine-learning capabilities while at Capgemini and worked in the Jaguar Land Rover Research Centre, where he was responsible for a variety of internal and external projects and contributed to the self-learning car portfolio.

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