Introduction to NLP | Word Embeddings & Word2Vec Model

Learn everything about word embeddings and word2vec model! I've explained CBOW and skip-gram models. I've also shown how to visualize higher dimensional word vectors in 2D.
#nlp #word2vec #machinelearning

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[harshpal]

The most underrated channel for Machine learning

[sreedharsree]

The best.. you elucidated this topic with charm !! Thanks Sujan

[TechResearch]

Very clear description. I was struggling to understand it but your video was very simple and provided required information

[ZohairAhmed]

Thanks, that is one of the best explanations, I Understand a lot.

[magelauditore]

Commenting after 1/3rd part f the video. It is really very clear. Wait up and continue this. You will get huge lots of subs. Keep it up

[debjyotibanerjee]

Really good explanation, now understood the concept

[manikant]

Beautiful Explanation, I love it!! 👍👍

[mastercomputersciencewitha]

Very Nice explaination sir. Thank you so much sir.

[shivanineeli]

please continue making NLP videos, please we want more and more if possible entire AI we would love u hear from you!

[swagatmishra]

Really awsum vdo.... So easy and clear explanation... Loved it..
Please make more vdos.. Thanks a lot..

[pratibhagoudar]

Thanks bruhhh🤍.... it's more clear dan compare regular classes # nlp

[edwardrouth]

Hi,
This could sound bit naive but i just want to know how did you figure out the parameter that you are passing to "api.load()" which is "word2vec-google-news-300". I mean there must be a list of API from where you got this right ? I googled it but i found there are just links and its bit confusing too.
Thanks.

[r_pydatascience]

Nice video.Does word2vec represent medical vocabularies? I have a medically text corpus that has about tokens. What do you think I should do?

[rexwan]

intro video remind me its wednesday my dudddde

[ccuuttww]

I want to ask a question is it all vector of words in same length?
Because I have an idea if we use DNA sequence(of course not in same length) instead of just words can we train a model to get a better classify result?

[coxixx]

please make a video about how back propagation works in skip gram.

[tobiascornille]

You said Skipgram predicts the context words from the target word, but then later you just compute the sigmoid (so not a softmax) to know if one pair of a target word and a context word is correct. I don't really see how this is "predicting" the context words.

Is there something else going on? I'm very confused since it seems like every explanation is saying something different...

[ThoTran-oixi]

Thank you so much for your video, can you turn on subtitles for this video? Because I'm not from England, I can't hear you clearly but the video has no subtitles

[krishcp]

Hi,

Your videos on NLP are great.
For, most_similar(positive = ['boy', 'queen'], negative='girl', topn=1) I am getting :
[('teenage_girl', 0.35459333658218384)]. What could be happening here? Krish

[debjyotibanerjee]

Bro just tell me one thing, while creating vectors of the words, do we need to remove stopwords, and lemmatize our text data, cause I believe if we do the mentioned text pre-processing steps, then may be the word2vec model may be not able to understand the context, and the training will not happen properly. If you could say something, that would help me a lot in my project.