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Daniel Lyons' Notes

Vectoring Words (Word Embeddings) - Computerphile

Description

How do you represent a word in AI? Rob Miles reveals how words can be formed from multi-dimensional vectors - with some unexpected results.

08:06 - Yes, it's a rubber egg :)

Unicorn AI:
EXTRA BITS: https://youtu.be/usthqKtw2LA
AI YouTube Comments: https://youtu.be/XyMdpcAPnZc

More from Rob Miles: http://bit.ly/Rob_Miles_YouTube

Thanks to Nottingham Hackspace for providing the filming location: http://bit.ly/notthack

https://www.facebook.com/computerphile
https://twitter.com/computer_phile

This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: https://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

My Notes

  • Subject: embeddings
  • 05:07: Two words are similar if they are often used in similar context.
  • 05:39: The problem that word embeddings is trying to solve is How do you represent words as vectors such that two similar words makes two similar vectors.

Transcript

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Vectoring Words (Word Embeddings) - Computerphile
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