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Advancing Autonomous Vehicle Development Using Distributed Deep Learning with Adrien Gaidon - TWiML Talk #269

2019-05-28 · 48 min · episode 269 · 13 entities

Asserted relationships

  • evidence rules-v4
    Advancing Autonomous Vehicle Development Using Distributed Deep Learning with Adrien Gaidon - TWiML Talk #269
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    evidence rules-v4
    Feed author/publisher: Sam Charrington
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    Feed category: Tech News
  • → hosted by TWIML company
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    evidence rules-v4
    Feed author/publisher: TWIML

Entities found in this episode

concepts 5

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    0.50
    evidence rules-v4
    Feed category: Science
  • mentioned Tech News concept
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    evidence rules-v4
    Feed category: Tech News
  • discusses Science concept
    0.40
    evidence rules-v4
    Feed category: Science
  • discusses News concept
    0.40
    evidence rules-v4
    Feed category: News
  • discusses Tech News concept
    0.40
    evidence rules-v4
    Feed category: Tech News

companys 4

  • mentioned TWIML company
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    Feed author/publisher: TWIML
  • mentioned Technology company
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    evidence rules-v4
    Feed category: Technology
  • discusses Technology company
    0.40
    evidence rules-v4
    Feed category: Technology
  • hosted by TWIML company
    0.40
    evidence rules-v4
    Feed author/publisher: TWIML

persons 3

  • mentioned Adrien Gaidon person
    0.72
    evidence rules-v4
    Advancing Autonomous Vehicle Development Using Distributed Deep Learning with Adrien Gaidon - TWiML Talk #269
  • mentioned Sam Charrington person
    0.70
    evidence rules-v4
    Feed author/publisher: Sam Charrington
  • hosted by Sam Charrington person
    0.55
    evidence rules-v4
    Feed author/publisher: Sam Charrington

podcasts 1

Episode description as stored
In this, the kickoff episode of AI Platforms Vol. 2, we're joined by Adrien Gaidon, Machine Learning Lead at Toyota Research Institute. Adrien and I caught up to discuss his team’s work on deploying distributed deep learning in the cloud, at scale. In our conversation, we discuss:  • The beginning and gradual scaling up of TRI's platform. • Their distributed deep learning methods, including their use of stock Pytorch, and much more!