For the negative sampling, the negative examples are word pairs with the same focus word for a number of noisy context words randomly sampled. But here it is done in a reverse way. Please let me know if the two ways are the same or it is a mistake here.
orange sweater over orange polo - my man is rocking the full lobster swagger
Exceptionally well done. Thank you!
Nice presentation, perfect blend of pace, voice quality and slide data.
Great explanation of W2V especially NS...
Nice explanation of NLP terms. I would like to learn more in terms of probability distribution and it's effect on some real data set.
This is an amazing video. Very intuitive. Thank you.
thank you for the video! Very helpful!
Best explanation ever watch; much better than Stanford lecture in my opinion.
Great explanation!
For the negative sampling, the negative examples are word pairs with the same focus word for a number of noisy context words randomly sampled. But here it is done in a reverse way. Please let me know if the two ways are the same or it is a mistake here.
best word2vec explanation I have seen so far
Your video helps me a lot.
Good god, it's nice to watch an informative video not done in the style of Siraj.
Nice explanation and thank you!
great explanation
I can't believe I already watched all these videos somehow. Oh wait, there's a partial red bar on the bottom of most thumbnails for some reason. 😋
I'm still confused about n-gram model and skip-ngram model.
Hi, My name is Ari. i am from Indonesia.
10:13
Great explanation