Word2Vec Simplified|Word2Vec explained in simple language|CBOW and Skipgrm methods in word2vec

[@abdullahilawal3220]

Masha ALLAH, i have searched for three years to get a simple explanation of w2v until i found your video today. U have done a great job. Thanks alot

[@UnfoldDataScience]

Your comment made my day Abdullahi. thanks for such motivation. keep watching.

[@metalhamster14]

You have a gift. A natural teacher.

[@debbs_io]

Finally a simple, clear and complete explanation of this word embeddings. Thank you so much. I've studied and searched for months and you've just made the whole thing clear and straight forward.

[@victorsolomonmachinelearning]

I've been searching for a comprehensive explanation without the technical jargons. you are the best teacher

[@UnfoldDataScience]

Thanks Solomon.

[@rajaramparimi]

very simple and clean explanation. Awesome

[@LocksRevenge]

Simplified and concise, Thank you Sir.

[@SaHaRaSquad]

Finally an understandable explanation of how those methods work. Short and to the point, thanks for this video.

[@pranjalgupta9427]

Nice explanation

[@UnfoldDataScience]

Keep watching Pranjal.

[@dub161]

crazzzzyyy. You make things so simple!!!!

[@shwetsgautam8079]

Very well explained.

[@jialaiz9101]

Very intuitive, thx man

[@UnfoldDataScience]

welcome Jialai.

[@RohanPaul-AI]

As always, superbly simple and great explanation. Many thanks.

[@UnfoldDataScience]

Welcome Rohan.

[@henriquemaulerborges1390]

You video is very simple to undestand, thank you man!

[@UnfoldDataScience]

Welcome

[@pankajuprety9216]

your explanation is simple clear and best

[@UnfoldDataScience]

Thanks Pankaj.

[@omkarjadhav13]

Good work.

[@UnfoldDataScience]

Thank you Omkar.

[@aniketwagh570]

awesome video sir, really awesome.

[@UnfoldDataScience]

So nice of you Aniket. Your comments are precious for me.

[@nikitasharma4957]

Jaise hi word or vector m difference smjh aaya i 👍 ❤️ like it seriously..after that I HV seen full vedio..osam explanation

[@caoilim]

I really don't comment but this is just great. Really helped me out with my college assignment. I was tearing my hair out before this. Genuinely thank you.

[@UnfoldDataScience]

Glad it helped!

[@arcanepersona1676]

May Allah increase your knowledge my friend, i am just starting with NLP and you help me learn the basics well. I didnt know what one hot embedding means. Thanks and keep it up. Also could you please do a project end to end in NLP for example sentiment analysis ? I want to see these techniques in real action. Thanks again :)

[@sharatchandra2045]

good and simple explaanation

[@UnfoldDataScience]

Thanks for liking Sharat.

[@vaishnavibhosale2272]

great work 👍👍

[@fatemeazizi2224]

simply explained, Thank you

[@bekiofficialchannel9746]

Thank you! I got the basic concepts about word2vec.

[@suhanshupatel9204]

Thanku bhaiyya.

[@UnfoldDataScience]

Welcome Suhanshu.

[@mrdh927]

Thank you, Aman, I really appreciate your simple explanation.

[@UnfoldDataScience]

Most Welcome. Your comments are precious for me.

[@krishnab6444]

Perfectly Explained , hats off to u!! Thank You

[@UnfoldDataScience]

You're most welcome

[@MrBansal907]

Good job sir, thank you

[@ShahzadKhan-zq9ty]

one of the finest explanation, thanks bro

[@UnfoldDataScience]

Thank u.

[@SallyMwalrMusic]

Just sending love and regards seem like a good person and appreciating your efforts to teach others,

[@vijaym4598]

Very simplified about Wrod2vec....Thanks and all the best

[@UnfoldDataScience]

Glad you liked it

[@mahikhan5716]

great aman . tactics of ur teaching is really tremendous

[@SACHINKUMAR-zo5pm]

I find ur videos very helpful, many topics which are not explained well in other channels, i come to ur channel to see if it is available and u have never disappointed me. Keep up the good work. Thanks.

[@UnfoldDataScience]

Thanks Sachin. Please share with friends also

[@sandipansarkar9211]

finished watching

[@sunilupadhyay7948]

very informatics pls continue.

[@UnfoldDataScience]

Thank you Sunil, I will

[@udayradhe6537]

Amazing! so well explained in simple terms.

[@UnfoldDataScience]

Thanks Uday

[@sandipansarkar9211]

great explanation

[@UnfoldDataScience]

Thanks Sandipan.

[@ribamarsantarosa4465]

humble and nice presentation :)

[@UnfoldDataScience]

Thanks a lot.

[@uwaisahamedimad556]

I have written the same in one of your previously watched video,but still I want to write it...You are really unfolding Data Science.

[@UnfoldDataScience]

Thanks Uwais. Your words mean a lot.

[@karthickd537]

Very nicely explained in a very simple way.

[@UnfoldDataScience]

Thanks Karthick.

[@NightSurge.]

Great video, straight to the point and easy to understand.

[@UnfoldDataScience]

Cheers.

[@dhlpaket2247]

Best man

[@UnfoldDataScience]

Thanks again.

[@gurmindersinghmalhotra8184]

Aman bhai, thanks for excellent explanation.

[@UnfoldDataScience]

Always welcome Gurminder Bhai.

[@vittalhundarad1644]

thank you sir , nice explanation

[@RezoanurRahman]

Literally can not make it more simpler than this.

[@UnfoldDataScience]

Thanks a lot Rahman.

[@RaghavaIndra]

Very good explanation!

[@UnfoldDataScience]

Thanks Raghava.

[@AbhishekKumar-hh5oh]

well explained and in a simplified way. Thanks a lot for this video

[@UnfoldDataScience]

Glad it was helpful Abhishek.

[@exuberantyouth8765]

Thanks Aman

[@SibanandaPattanaik]

@Unfold Data Science In the starting whatever encoding ur doing, I don't think it is one hot encoding instead it's a BOW representation...Correct me if i m wrong.

[@UnfoldDataScience]

wil check

[@NawasNaziru]

That's one-cold embedding

[@learn-and-live]

thank you for this clear explanation!

[@UnfoldDataScience]

Thank you

[@LearningWithFun333]

Great explanation. thanks a lot.

[@UnfoldDataScience]

You are welcome! Tabassum.

[@findyourpassion1677]

Very helpful explanation. Looking forward to seeing more. Thanks in advance.

[@UnfoldDataScience]

Glad it was helpful!

[@juniormedjeufopa3780]

Thanks :) Great explanations

[@UnfoldDataScience]

Welcome.

[@piyalikarmakar5979]

Thank you so much sir.. From the documentations I was confused.. Thanks a lot for such cleared and easy explanation..

[@UnfoldDataScience]

Welcome Piyali.

[@shivnathsinghgaur5972]

i spend too much time to get logic behind woed2vec but now i am satisfy

[@UnfoldDataScience]

Thanks for watching.

[@unexpected2004]

Great stuff! Couldn't find a better video with such well assembled content.

[@UnfoldDataScience]

Glad it was helpful!

[@the_street_coder4433]

Owosome Men, I Loved the explanation.

[@UnfoldDataScience]

Glad you liked it

[@linxin3811]

Very good introduction to Word2Vec. Would have preferred a bigger writing space, but nonetheless was very clear

[@UnfoldDataScience]

Glad it was helpful Kevin.

[@שחרכהן-פ6ד]

thank you!

[@UnfoldDataScience]

You're welcome!

[@sudiptamang1018]

thank you

[@UnfoldDataScience]

You're welcome Sudip.

[@rejithmadhavan5735]

Precise explanation. Very useful. Thank you Aman. Just one suggestion, visually it will be more soothing if you could follow straight line on your whiteboard.

[@UnfoldDataScience]

Thanks a lot for suggestion.

[@preranatiwary7690]

Good one again!

[@UnfoldDataScience]

Thanks again! Happy Learning!

[@smartrider.strider]

Great stuff!

[@UnfoldDataScience]

Thanks Aditya.

[@pakistantechknowledge3911]

you mean sparse vector of word will give dense vector. how features are selected and how values are assigned ?

[@anurodhchoudhary1689]

when Legends start to teach everything becomes ezzzz!!!!!

[@UnfoldDataScience]

Thanks Anurodh.

[@binaykumar1616]

You explained nicely. Please use a bigger screen.

[@UnfoldDataScience]

Thanks for watching and Feedback Binay.

[@syedharis3235]

Great job bro!

[@UnfoldDataScience]

Thanks for the visit Syed.

[@omarwaleedsalihalrassam905]

Great explanation and well done, my question is how the numbers can be selected to set the features? I mean, in your example at time (5:00) of your video, you assigned 0.9 to the apple and 0.85 to mango ..etc, how these numbers can be assigned?

[@UnfoldDataScience]

We do not assign manually, framework assigns.

[@saqibcs]

Brother, great explanation. I will appreciate if could just tell where I can found next video

[@UnfoldDataScience]

Please check in my playlist for NLP

[@shaheenullah3212]

Great sir. please sir also share about pos tagging using this approach

[@UnfoldDataScience]

Sure, will do Shaheen.

[@claudeclaude9991]

Man, you are great. Thanks a lot. Do you have any doc2vec video or playlist?

[@UnfoldDataScience]

Thanks a lot, doc2vec, I will create. Have not created yet.

[@piyalikarmakar5979]

Sir, I have one query that as input we need to feed the one hot representation of the surrounding words including the one hot vector of the target word or excluding so?

[@UnfoldDataScience]

Hi Piyali. Excluding. For target you might need to do separate encoding.

[@piyalikarmakar5979]

@@UnfoldDataScience okay sir..then in case of getting all the word embeddings of the input sentence..one hot encode of the words under the window passed through CBOW and find the middle target word using the feature based information.. then slide the window and repeat the same untill the end... is it the process sir?

[@anagham2413]

How to increase the Dimensionality of feature vector

[@vijethrai2747]

By increasing output size more than 300 units you can get vector with higher dimensions and increase the model quality

[@UnfoldDataScience]

THanks Vijeth.

[@aqsashahzad5139]

Informative video... (y) Or in m sy best algorithm konsa h text classification tasks k liy cbow ya skip gram?

[@UnfoldDataScience]

Depends on what kind of data you are working for.

[@sidhantgi899]

Sir aap ne graduation kiss subject se kiya hai.

[@UnfoldDataScience]

Hi Rawat, I am BTECH.

[@sidhantgi899]

@@UnfoldDataScience B.tech Mechnical ya koi aur

[@sidhantgi899]

@@UnfoldDataScience ??

[@nagithareddy1798]

How to write a program to implement continuous bag of words model using knn algorithm (using python).

[@UnfoldDataScience]

Hi Nagitha, You can convert word to number and run KNN.

[@nagithareddy1798]

@@UnfoldDataScience could u please tell in detail I'm new to this course I donno how to do if possible please provide the code with explanation

[@mahikhan5716]

i have one question since bag of words has issue so isn't it used anywhere ?

[@UnfoldDataScience]

Mostly not used as it's very simple technique

[@prashanthkolaneru3178]

great stuff, where this application mostly used?

[@UnfoldDataScience]

Thanks Prashanth, in many NLP application where we need to capture semantic meaning of sentence.

[@gururajraykar9254]

How Word2Vec will handle for Test data set?

[@UnfoldDataScience]

We need to create word vector for test and train in similar way.

[@omarsalam7586]

[@paragpujari3961]

please do not put ads in between it creates distraction otherwise lecture is good

[@maYYidtS]

bro how can we decide vector space size ex. (5000 words*300 vectors)...here 5000 is our vocab size, but how can we decide the 300 lengths...

[@UnfoldDataScience]

Normally we take 300 dimension however if our vocab size is huge, then processing might be difficult with more number of dimension hence we try limiting it to 200 or so. If you do not have infra issue, you can go for 500 or even more.

[@maYYidtS]

@@UnfoldDataScience Thanks....bro

[@maYYidtS]

@@UnfoldDataScience so it is considered as a hyperparameter?...can we change this in tunning. and please make video on optuna, biasen hyperparameter tunnign.

[@suriebc6779]

Hi, I have an experience of 8 years in IT(Programming) and started preparing for data science career transition. My query is How can I put up in Resume. Shall I tell them that, I am new to this and Know all the concept and worked on kaggle datasets? Will they allow or reject since no past experience on data science? looking forward for your valuable comments.

[@UnfoldDataScience]

Hi, The query needs some bit of understanding your profile and giving suggestion I see you are looking for some career guidance, If yes, you can reach me directly for one on one mentorship. You can go to "About" section in the channel and click the link "One to One Mentorship Link"

[@vihangafernando6644]

Are you using CBOW or Skipgram

[@UnfoldDataScience]

I Think CBOW, not sure though, the default one.

[@trexmidnite]

is nothing BUTT!

[@UnfoldDataScience]

Cheers 🍻