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Since Keras API as defined as layers, how would it be used to implement the word2vec?

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  • $\begingroup$ Hi. This question is more suited to DataScience. $\endgroup$ – Philip Raeisghasem Mar 29 at 17:39
  • $\begingroup$ @PhilipRaeisghasem Is it possible this question is broad enough to qualify as "theory"? $\endgroup$ – DukeZhou Mar 29 at 20:42
  • $\begingroup$ I think any complete answer would need to reference the Keras API, so I still vote to close as off-topic. $\endgroup$ – Philip Raeisghasem Mar 29 at 21:07
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Your question is really broad.. But there are many manuals and tutorials which describe how to do it step by step.

My personal favorite would be:

Practical Text Classification With Python and Keras - It has a very detailed explanation of every step of the implementation while remaining practical.

You can also try:

A Word2Vec Keras tutorial - This one focuses more on understanding how word2vec embedding works, but it also contains an implementation.

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Tensorflow released a version, however, it's missing negative sampling, which is very important in word2vec

https://www.tensorflow.org/tutorials/text/word_embeddings

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