Difference between revisions of "Continuous Bag-of-Words (CBoW)"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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| − | [http://www.youtube.com/results?search_query=Bag+Words+nlp+natural+language YouTube search...] | + | [http://www.youtube.com/results?search_query=Continuous+Bag+Words+cbow+nlp+natural+language YouTube search...] |
| − | [http://www.google.com/search?q=Bag+Words+nlp+natural+language ...Google search] | + | [http://www.google.com/search?q=Continuous+Bag+Words+cbow+nlp+natural+language ...Google search] |
* [[Bag-of-Words (BOW)]] | * [[Bag-of-Words (BOW)]] | ||
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* [[Word2Vec]] | * [[Word2Vec]] | ||
* [[Skip-Gram]] | * [[Skip-Gram]] | ||
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The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words). Considering a simple sentence, “the quick brown fox jumps over the lazy dog”, this can be pairs of (context_window, target_word) where if we consider a context window of size 2, we have examples like ([quick, fox], brown), ([the, brown], quick), ([the, dog], lazy) and so on. Thus the model tries to predict the target_word based on the context_window words. [http://towardsdatascience.com/understanding-feature-engineering-part-4-deep-learning-methods-for-text-data-96c44370bbfa A hands-on intuitive approach to Deep Learning Methods for Text Data — Word2Vec, GloVe and FastText | Dipanjan Sarkar - Towards Data Science] | The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words). Considering a simple sentence, “the quick brown fox jumps over the lazy dog”, this can be pairs of (context_window, target_word) where if we consider a context window of size 2, we have examples like ([quick, fox], brown), ([the, brown], quick), ([the, dog], lazy) and so on. Thus the model tries to predict the target_word based on the context_window words. [http://towardsdatascience.com/understanding-feature-engineering-part-4-deep-learning-methods-for-text-data-96c44370bbfa A hands-on intuitive approach to Deep Learning Methods for Text Data — Word2Vec, GloVe and FastText | Dipanjan Sarkar - Towards Data Science] | ||
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<youtube>yBmtXtVya9A</youtube> | <youtube>yBmtXtVya9A</youtube> | ||
<youtube>UqRCEmrv1gQ</youtube> | <youtube>UqRCEmrv1gQ</youtube> | ||
| + | <youtube>uskth3b6H_A</youtube> | ||
<youtube>cNnqdz_L-eE</youtube> | <youtube>cNnqdz_L-eE</youtube> | ||
Revision as of 14:15, 12 July 2019
YouTube search... ...Google search
The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words). Considering a simple sentence, “the quick brown fox jumps over the lazy dog”, this can be pairs of (context_window, target_word) where if we consider a context window of size 2, we have examples like ([quick, fox], brown), ([the, brown], quick), ([the, dog], lazy) and so on. Thus the model tries to predict the target_word based on the context_window words. A hands-on intuitive approach to Deep Learning Methods for Text Data — Word2Vec, GloVe and FastText | Dipanjan Sarkar - Towards Data Science
