Difference between Gensim word2vec and keras Embedding layer
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02-11-2019 - |
문제
I used the gensim word2vec
package and Keras Embedding layer
for various different projects. Then I realize they seem to do the same thing, they all try to convert a word into a feature vector.
Am I understanding this properly? What exactly is the difference between these two methods?
Thanks!
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