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Low code, no code, accelerated code, & failing code
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Daniel Whitenack and Chris Benson

· 48 Minutes

PodcastPractical AI

Low code, no code, accelerated code, & failing code

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In this Fully-Connected episode, Chris and Daniel discuss low code / no code development, GPU jargon, plus more data leakage issues. They also share some really cool new learning opportunities for leveling up your AI/ML game!

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Featuring:

  • Chris Benson – Website, GitHub, LinkedIn, X
  • Daniel Whitenack – Website, GitHub, X

Show Notes:

  • Follow up content from Rajiv Shah: 
    • Running code and failing models
    • Rajiv’s previous episode
  • Lambda Lab’s GPU benchmarks
  • Machine Learning in Microsoft Excel
  • Deep Learning at the Speed of Light
  • MLCommons and MLCube: 
    • Previous episode about MLCommons
    • MLCube project
  • Learning Resources: 
    • Yann LeCun’s Deep Learning Course Is Now Free & Fully Online
    • TensorFlow Everywhere

Upcoming Events: 

  • Register for upcoming webinars here!

Original source

This story was published by Practical AI and written by Daniel Whitenack and Chris Benson. SyncAI.news shows a preview; the complete article is on the publisher's site.

Read the full story on share.transistor.fm

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