Word2Vec : Natural Language Processing

How do we turn words into vectors?

[matthewson]

Finally, someone made a clear and concise introduction for Word2Vec! I admire you!

[revathik]

Great video. One thing to add: instead of always discarding the context vectors at the end, another strategy (mentioned in other videos/articles) is to concatenate or add the two vectors for a word.

[YingleiZhang]

You are a genius! You are able to explain abstract things well with only white board (no animation!)

[yangwang]

Great explanation, it’s pure gold! Can you make a video in terms of hierarchical softmax? It confuses me for a long time.

[a]

This is amazing!! I remember trying to understand this back when it was first published, and i failed so hard...thank you

[drsandeepvm]

Exceptionally simplified explanation. Thanks 😊

[sefinehtesfa]

I appreciate your explanations.
I was stuck on Word2Vec. However, you explainde this more than enough.
Thank you so much!!!

[jett_royce]

Great video. You mentioned that we discard the context vectors and take the main vectors as our final word embeddings. I just want to add that based on the literature, you can also add the main and context vectors, or concatenate them.

[DC-tqkh]

Probably the best video explanation I watched. thank you.

[DennisRice-lhnd]

The best and most concise explanation of Word2Vec that I've seen so far. I probably need to go back and review gradient descent again, because updating the weights is still confusing.

[nobodyelse]

You're so good! Thank you very much for this explanation, my minds are so slear after it, magic!

[bharathtvadhoola]

Awesome video! numerical example was particularly helpful. Cheers :)

[dominicprior]

Wow, such a nice clear video! Many thanks!

[saikatroy]

Explained in lucid manner. Nice video.

[zhiyili]

Thank you for the video! Great content!

[vctorroferz]

Incredible and amazing explanation! Thanks so much for such great content!

[junekang]

Great video! Thank you for explaining this so clear.

[Recoils]

This explanation is so well done! Thank you!

[MikeKittelberger]

Awesome explanation, you safe me a lot of time. Thank you! :)

[Halo-uznd]

Another fantastic video! The main/context embeddings kind of confused me but the ending really cleared it up. Curious to know if there is a deliberate way of choosing # of dimensions or if its simply trial and error. On a side note, will you be participating in the "66 days of data" challenge this July?