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A word embedding is a learned representation for text where words that have the same meaning have a similar representation. It is this approach to representing words and documents that may be considered one of the key breakthroughs of deep learning on challenging natural language processing problems.

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Klien Mañago says:

Good day, may I ask how to define specific dimensions of features (for example, I want to extract linguistic features such as part of speech tagging, word density, and word frequency) that is going to be vectorized?

Mohammad Kazemi Beydokhti says:

Thanks for the tutorial on word embedding models. I wonder how are features selected in these models? I think in some particular cases having control on customizing these feature might enhance the chance of getting more similar words than just using the pretarined ones.

Nazwa Kurde says:

thanks this is awesome

José Roberto Homeli da Silva says:

unfortunately, I cannot like your video 300 times <3

louer le seigneur says:

Thanks Krish

Usashi Chatterjee says:

Sir your explanations are fantastic

Utkarsh says:

His channel and Statquest are 2 of the best resources for ML and Data Science on Youtube.

Suketu Dave says:

Pen Pineapple Apple Pen 🙂


good explanation sir

Muhammad Noman Khan, Assistant Professor, Department of Journalism and Mass Communication, UoP says:

Excellent tutorials .

suvarna deore says:

Thank you sir

Aditya Chauhan says:


Ghizlane BOUSKRI says:

Krish, you save my life every time

Yang Li says:

you are 200% better than my professor in explaining Word Embedding

trex midnite says:

How to convert man to woman?

MavaaMusicMachine says:

Amazing explanation thank you!

R_Py Data Science says:

I don’t really get word embedding. Does it work outside the mainstream English language? For example medical language is different. If I am studying about medical literature, a lot of my main vocabularies are medical words. What is your opinion on this?

Dharmendra Thakur says:

I have one doubt can you make it clear, my doubt is how it is assigning the values for opposite genders, like for Boy -1 and for girl 1

Dharmendra Thakur says:

You have done a great job, There are students like me, who really need such explannation for getting a rough image…atleast thanks a lot

Sandeep says:

Many thanks

Golden Water says:

I wish you could shorten the videos .

Dinesh Babu says:

someone please explain me what is vocabulary and vocabulary size . I'm confused with these terms.

Patrick Adjei says:

I don't usually subscribe to youtube channels … but this first video I watched from you got me.

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