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.

[@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.

[@metalhamster14]

You have a gift. A natural teacher.

[@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.

[@henriquemaulerborges1390]

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

[@UnfoldDataScience]

Welcome

[@nikitasharma4957]

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

[@dub161]

crazzzzyyy. You make things so simple!!!!

[@pankajuprety9216]

your explanation is simple clear and best

[@UnfoldDataScience]

Thanks Pankaj.

[@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 :)

[@SallyMwalrMusic]

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

[@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.

[@bekiofficialchannel9746]

Thank you! I got the basic concepts about word2vec.

[@mahikhan5716]

great aman . tactics of ur teaching is really tremendous

[@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!

[@krishnab6444]

Perfectly Explained , hats off to u!! Thank You

[@UnfoldDataScience]

You're most welcome

[@vijaym4598]

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

[@UnfoldDataScience]

Glad you liked it

[@mrdh927]

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

[@UnfoldDataScience]

Most Welcome. Your comments are precious for me.

[@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

[@RezoanurRahman]

Literally can not make it more simpler than this.

[@UnfoldDataScience]

Thanks a lot Rahman.

[@jialaiz9101]

Very intuitive, thx man

[@UnfoldDataScience]

welcome Jialai.

[@shivnathsinghgaur5972]

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

[@UnfoldDataScience]

Thanks for watching.

[@RohanPaul-AI]

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

[@UnfoldDataScience]

Welcome Rohan.

[@fatemeazizi2224]

simply explained, Thank you

[@udayradhe6537]

Amazing! so well explained in simple terms.

[@UnfoldDataScience]

Thanks Uday

[@ShahzadKhan-zq9ty]

one of the finest explanation, thanks bro

[@UnfoldDataScience]

Thank u.

[@gurmindersinghmalhotra8184]

Aman bhai, thanks for excellent explanation.

[@UnfoldDataScience]

Always welcome Gurminder Bhai.

[@linxin3811]

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

[@UnfoldDataScience]

Glad it was helpful Kevin.

[@karthickd537]

Very nicely explained in a very simple way.

[@UnfoldDataScience]

Thanks Karthick.

[@MrBansal907]

Good job sir, thank you

[@pranjalgupta9427]

Nice explanation

[@UnfoldDataScience]

Keep watching Pranjal.

[@NightSurge.]

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

[@UnfoldDataScience]

Cheers.

[@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.

[@vittalhundarad1644]

thank you sir , nice explanation

[@AbhishekKumar-hh5oh]

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

[@UnfoldDataScience]

Glad it was helpful Abhishek.

[@piyalikarmakar5979]

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

[@UnfoldDataScience]

Welcome Piyali.

[@aniketwagh570]

awesome video sir, really awesome.

[@UnfoldDataScience]

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

[@ribamarsantarosa4465]

humble and nice presentation :)

[@UnfoldDataScience]

Thanks a lot.

[@learn-and-live]

thank you for this clear explanation!

[@UnfoldDataScience]

Thank you

[@sunilupadhyay7948]

very informatics pls continue.

[@UnfoldDataScience]

Thank you Sunil, I will

[@sandipansarkar9211]

finished watching

[@omkarjadhav13]

Good work.

[@UnfoldDataScience]

Thank you Omkar.

[@findyourpassion1677]

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

[@UnfoldDataScience]

Glad it was helpful!

[@vaishnavibhosale2272]

great work 👍👍

[@RaghavaIndra]

Very good explanation!

[@UnfoldDataScience]

Thanks Raghava.

[@sharatchandra2045]

good and simple explaanation

[@UnfoldDataScience]

Thanks for liking Sharat.

[@exuberantyouth8765]

Thanks Aman

[@suhanshupatel9204]

Thanku bhaiyya.

[@UnfoldDataScience]

Welcome Suhanshu.

[@unexpected2004]

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

[@UnfoldDataScience]

Glad it was helpful!

[@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

[@dhlpaket2247]

Best man

[@UnfoldDataScience]

Thanks again.

[@LearningWithFun333]

Great explanation. thanks a lot.

[@UnfoldDataScience]

You are welcome! Tabassum.

[@the_street_coder4433]

Owosome Men, I Loved the explanation.

[@UnfoldDataScience]

Glad you liked it

[@juniormedjeufopa3780]

Thanks :) Great explanations

[@UnfoldDataScience]

Welcome.

[@sandipansarkar9211]

great explanation

[@UnfoldDataScience]

Thanks Sandipan.

[@binaykumar1616]

You explained nicely. Please use a bigger screen.

[@UnfoldDataScience]

Thanks for watching and Feedback Binay.

[@anurodhchoudhary1689]

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

[@UnfoldDataScience]

Thanks Anurodh.

[@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.

[@shaheenullah3212]

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

[@UnfoldDataScience]

Sure, will do Shaheen.

[@sudiptamang1018]

thank you

[@UnfoldDataScience]

You're welcome Sudip.

[@pakistantechknowledge3911]

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

[@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

[@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.

[@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.

[@שחרכהן-פ6ד]

thank you!

[@UnfoldDataScience]

You're welcome!

[@preranatiwary7690]

Good one again!

[@UnfoldDataScience]

Thanks again! Happy Learning!

[@smartrider.strider]

Great stuff!

[@UnfoldDataScience]

Thanks Aditya.

[@syedharis3235]

Great job bro!

[@UnfoldDataScience]

Thanks for the visit Syed.

[@prashanthkolaneru3178]

great stuff, where this application mostly used?

[@UnfoldDataScience]

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

[@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?

[@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"

[@omarsalam7586]

[@paragpujari3961]

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

[@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

[@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.

[@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 ??

[@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.

[@gururajraykar9254]

How Word2Vec will handle for Test data set?

[@UnfoldDataScience]

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

[@vihangafernando6644]

Are you using CBOW or Skipgram

[@UnfoldDataScience]

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

[@trexmidnite]

is nothing BUTT!

[@UnfoldDataScience]

Cheers 🍻