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A One Hot Vector: Difference between revisions

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(Note that)
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=one-hot-vectors=
 
=one-hot-vectors=
  
"Meaning is this illusive thing that were trying to capture" (Richard Socher in [https://www.youtube.com/watch?v=xhHOL3TNyJs&index=2&list=PLcGUo322oqu9n4i0X3cRJgKyVy7OkDdoi CS224D Lecture 2 - 31st Mar 2016 (Youtube)])
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"''Meaning is this illusive thing that were trying to capture''" (Richard Socher in [https://www.youtube.com/watch?v=xhHOL3TNyJs&index=2&list=PLcGUo322oqu9n4i0X3cRJgKyVy7OkDdoi CS224D Lecture 2 - 31st Mar 2016 (Youtube)])
  
 
[0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]  
 
[0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]  
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=Note that=
 
=Note that=
''Words are represented once in a vector. So words with multiple meanings, like "bank", are more difficult to represent. There is research to multivectors for one word, so that it does not end up in the middle.'' - Richard Socher, for more notes on this lecture visit http://pad.constantvzw.org/public_pad/neural_networks_3
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"''Words are represented once in a vector. So words with multiple meanings, like "bank", are more difficult to represent. There is research to multivectors for one word, so that it does not end up in the middle.''" (Richard Socher in [https://www.youtube.com/watch?v=xhHOL3TNyJs&index=2&list=PLcGUo322oqu9n4i0X3cRJgKyVy7OkDdoi CS224D Lecture 2 - 31st Mar 2016 (Youtube)]
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For more notes on this lecture visit http://pad.constantvzw.org/public_pad/neural_networks_3
  
  
  
 
[[Category:Algoliterary-Encounters]]
 
[[Category:Algoliterary-Encounters]]

Revision as of 14:47, 25 October 2017

Type: Algoliterary exploration
Technique: word-embeddings
Developed by: Algolit

one-hot-vectors

"Meaning is this illusive thing that were trying to capture" (Richard Socher in CS224D Lecture 2 - 31st Mar 2016 (Youtube))

[0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]

with one 0 for each word in a vocabulary where the 1 is representing the place of a word in the vector > In this kind of vector representation: none of the words are similar, they are all a 1.

Note that

"Words are represented once in a vector. So words with multiple meanings, like "bank", are more difficult to represent. There is research to multivectors for one word, so that it does not end up in the middle." (Richard Socher in CS224D Lecture 2 - 31st Mar 2016 (Youtube)

For more notes on this lecture visit http://pad.constantvzw.org/public_pad/neural_networks_3