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Word2Vec - Skipgram and CBOW 

The Semicolon
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#Word2Vec #SkipGram #CBOW #DeepLearning
Word2Vec is a very popular algorithm for generating word embeddings. It preserves word relationships and is used with a lot of Deep Learning applications.
In this video we will learn about the working of word2vec and word embeddings. We will also learn about Skipgram and Continuous bag of words (CBOW ) which help in generating word2vec embeddings.
Word2Vec coupled with RNNs and CNNs are also used in building chatbots. They have lots of other use cases too.
Introduction: (0:00)
Why use word embeddings?: (0:14)
What is Word2vec?: (0:42)
Working of Word2vec?: (1:58)
CBOW and skipgram?: (2:48)
CBOW working ?: (3:36)
skip gram working ?: (5:32)

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5 авг 2024

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Комментарии : 132   
@nax2kim2
@nax2kim2 3 года назад
indexing for me 2:40 Word2Vec exam 3:06 CBOW 3:20 Skip Gram ----- 5:30 CBOW - working 5:50 Skip Gram - working 6:30 Getting word embeddings thx for this video :)
@rma1563
@rma1563 5 месяцев назад
By far the best explanation of this topic. It's crazy you only took 7 minutes to explain what most people spend a lot more and still can't deliver. Thanks ❤
@iindifferent
@iindifferent 4 года назад
Thank you. I was having a hard time understanding the concept from my uni and classes. After watching your video I went back and reread, and everything started to make more sense. Went back here watched this a second time and I think I have the hang of it now.
@TheSemiColon
@TheSemiColon 4 года назад
Glad it helped!
@user-fy5go3rh8p
@user-fy5go3rh8p 3 года назад
This is the best explanation I've encountered so far. Thank you!
@sheshagirigh
@sheshagirigh 5 лет назад
Thanks a ton. By far the best i could find after a lot of searching.. even better than few from stanford lectures!
@fabricesimodefo8113
@fabricesimodefo8113 4 года назад
Exactly what i was searching for ! so clear. Sometime you just need the neural network structure in details in graph or visually. Why don't many people do that ? Its the simplest way to understand what is happening in real in the code after
@TheSemiColon
@TheSemiColon 4 года назад
This is what I needed when I was creating it, but did not find it anywhere :)
@subhamprasad6808
@subhamprasad6808 3 года назад
Finally, I understood the concept of Word2Vec after watching this video. Thank you.
@GunturBudiHerwanto
@GunturBudiHerwanto 3 года назад
Thank you sir! I always come back to this video when I forgot about the concept.
@chihiroa1045
@chihiroa1045 Год назад
Thank you so much! This is the most clear and organized tutorial I found on Word2Vec!
@tylerlozano152
@tylerlozano152 4 года назад
Thank you for the thorough, simple explanation.
@jiexiong8522
@jiexiong8522 3 месяца назад
Other word2vec videos are still intimidating even after a lot of graph and simplification. Your video is so friendly and helped me understand this key algorithm. Thanks!
@maqboolurrahimkhan
@maqboolurrahimkhan 2 года назад
Best and easy explanation of word2vec over the internet. Keep up the good work Thanks a ton
@Amf313
@Amf313 2 года назад
Best explanation I saw through Internet to illustrate how Word2Vec works. Paper was a little bit hard to read; Andrew Ng's explanation was somewhat incomplete or at least ambigious to me, but your video made it clear. Thank you🙏
@jusjosef
@jusjosef 3 года назад
Very simple, to the point explanation. Beautiful!
@skipintro9988
@skipintro9988 3 года назад
Thanks, bro - this one is the easiest and simplest and quickest explanation on word2vec
@carlrobinson2926
@carlrobinson2926 5 лет назад
very nice explanation, not too long, straight to the point. thanks
@rainoorosmansaputratampubo2213
@rainoorosmansaputratampubo2213 3 года назад
Thank you so much. with this explanation I can understand it easier than read from books
@ajinkyajoshi2308
@ajinkyajoshi2308 2 года назад
Very well done!! Precise and to the point explanation!!
@satyarajadasara9000
@satyarajadasara9000 4 года назад
Very nice video where everything was to the point! Keep posting such wonderful content!
@bloodzitup
@bloodzitup 4 года назад
Thanks, my lecturer had this video in his references for learning word2vec
@absoluteanagha
@absoluteanagha 3 года назад
Love this! Such a great explanation!
@pushkarmandot4426
@pushkarmandot4426 4 года назад
The best video. Explained the whole concept in a very short amount of time
@MrStudent1978
@MrStudent1978 4 года назад
Absolutely beautiful explanation!! Very precise and very much informative....Thanks for your kindness. Sharing one's learning is the best thing that a person can do to contribute to the society. Lots of respects from Punjab India....
@TheSemiColon
@TheSemiColon 4 года назад
Glad it was helpful!
@OorakanaGleb
@OorakanaGleb 4 года назад
Awesome explanation. Thanks!
@ankursri21
@ankursri21 4 года назад
Thank you.. very well explained in shorter time.
@varunjindal1520
@varunjindal1520 3 года назад
This is indeed very good video. To the point and covers what I needed to know. Thank you.
@TheSemiColon
@TheSemiColon 3 года назад
Glad you found it useful, do share the word 🙂
@FTLC
@FTLC Год назад
Thank you so much is was so confused before watching this video ,now its clear to me
@MehdiMirzapour
@MehdiMirzapour 5 лет назад
Thanks. It is really a brilliant explanation!
@theunknown2090
@theunknown2090 5 лет назад
Hey in cobw and skip gram Method there are 3 Weight metrics Which metric is selected as d embedding matrix ? And why
@bryancamilo5139
@bryancamilo5139 5 месяцев назад
Thank you, your explanation is great. Now I have understood the concept 😁
@jamesmina7258
@jamesmina7258 29 дней назад
Thank you. I learned a lot from your video.
@MARTIN-101
@MARTIN-101 2 года назад
this was such an informative lecture, thank you.
@befesa1
@befesa1 2 месяца назад
Thank you! Really good explanation:)
@anujlahoty8022
@anujlahoty8022 Год назад
Simple and eloquent explanation.
@HY-nt8nk
@HY-nt8nk 3 года назад
Good work! Nicely explained.
@mohajeramir
@mohajeramir 3 года назад
this is the best explanation I have found. thank you
@TheSemiColon
@TheSemiColon 3 года назад
Glad you found it useful, do share the word 🙂
@aravindaraman8667
@aravindaraman8667 3 года назад
Amazing explanation! Thanks a lot
@coolbowties394
@coolbowties394 4 года назад
Thanks so much for this thorough explanation!
@TheSemiColon
@TheSemiColon 4 года назад
Glad it was helpful!
@nithin5238
@nithin5238 4 года назад
Very clear explanation man.. you deserve slow claps
@Zinghere
@Zinghere 2 года назад
Great explanation!
@tumul1474
@tumul1474 5 лет назад
awesome !!
@ashwinrameshbabu2418
@ashwinrameshbabu2418 3 года назад
At time 5.28, cbow , hope gives 1x3 and set gives 1x3 dimension output. How are they combined into 1 (1x3) before sending to final layer?
@AdityaPatilR
@AdityaPatilR 3 года назад
If hope can set us free hope can set you free as well !! thank you for the explanation and following what you preach ;)
@ogsconnect1312
@ogsconnect1312 4 года назад
I cannot say anything but excellent. Thank you
@haorao2464
@haorao2464 3 года назад
Thanks so much!
@pranabsarkar
@pranabsarkar 4 года назад
Thanks a lot!
@parthpatel3900
@parthpatel3900 5 лет назад
Wonderful video
@prathimads2876
@prathimads2876 5 лет назад
Thank you so much Sir...
@renessadesouza5601
@renessadesouza5601 3 года назад
Thank you so much
@hardikajmani5088
@hardikajmani5088 4 года назад
Very well explained
@mohajeramir
@mohajeramir 4 года назад
this was excellent. Thank you
@TheSemiColon
@TheSemiColon 4 года назад
Glad it was helpful!
@alialsaffar6090
@alialsaffar6090 5 лет назад
This was enlightening. Thank you!
@impracticaldev
@impracticaldev Год назад
You earned a subsciption. Good luck!
@himanshusrihsk4302
@himanshusrihsk4302 4 года назад
Really very useful
@sunjitrana374
@sunjitrana374 5 лет назад
Nice explanation, Thanks for that!!! One question: How to decide optimal length of hidden layer? here in example its 3 and in general you said it's around 300.
@johncompassion9054
@johncompassion9054 3 месяца назад
4:50 "5X3 input matrix is shared by the context words". what do you mean by input matrix? Do you mean the weight matrix between the hidden layer (embedding) and the output layer? 5:18 "You take the weight matrix and it becomes the set of vectors". We have two weight matrices so which one? Also, I guess our vector embedding is the middle layer output values not weights. Correct me if I am wrong. Thank you.
@md.prantohasan9630
@md.prantohasan9630 4 года назад
Excellent explanation in a very short time. Take
@sadeenmahbubmobin7102
@sadeenmahbubmobin7102 4 года назад
reading material ta bujhay de amre akhn :3
@aliqais4896
@aliqais4896 4 года назад
thank you very much
@juanpablo87t
@juanpablo87t 2 года назад
Great Video, thank you! It is very clear how to extract the word embeddings in skip gram by multipliying the W matrix with the one hot vector of the corresponding word, however I can't figure how to extract them from the CBOW model as there are multiple W matrixes, could you give me a hint or a maybe a resource where this is explained?
@anindyavedant801
@anindyavedant801 5 лет назад
I had a doubt, shouldn't the first weight matrix with which the input is multiplied be of dimensions 5x3 as all the connections need to be mapped to the hidden layer matrix and we have 5 inputs and 3 nodes in the hidden layer so the weights would be 5x3 and the second one would be vice versa i.e. 3x5
@ms10596
@ms10596 5 лет назад
So helpful
@BrunoCPunto
@BrunoCPunto 3 года назад
Awesome
@muhammedhassen4354
@muhammedhassen4354 5 лет назад
easy way explanation gr8
@vid_sh_itsme4340
@vid_sh_itsme4340 12 дней назад
is hierarchical softmax used in this?
@hashinitheldeniya1347
@hashinitheldeniya1347 3 года назад
can we cluster word phrases into groups using this word2vec technique?
@keno2055
@keno2055 2 года назад
Why does the hidden layer at 4:59 have 3 nodes if we only care about the 2 adjacent nodes?
@hs_harsh
@hs_harsh 5 лет назад
Sir can you provide the link of slides used. That would be helpful. I'm a student at IIT Delhi and I have to deliver a similar lecture presentation. Thank you!
@gauharahmad2643
@gauharahmad2643 5 лет назад
Sir what do we mean by size of each vector in 4:37 ?
@romanm7530
@romanm7530 2 года назад
Диктор просто огонь!
@Simply-Charm
@Simply-Charm 4 года назад
Thank you
@iliasp4275
@iliasp4275 3 года назад
thank you , The Semicolon.
@Hellow_._
@Hellow_._ 11 месяцев назад
how can we give all input vectors in one go to train the model?
@arnav3674
@arnav3674 3 месяца назад
Good !
@057ahmadhilmand6
@057ahmadhilmand6 8 месяцев назад
i still dont get it, the word vector for each word is a matriks?
@gouripeddivenkataasrithbha5148
@gouripeddivenkataasrithbha5148 4 года назад
Truly the best resource on word2vec by far. I have only one doubt. What do you mean by size of a vector being three. Other than this, I was able to understand everything.
@TheSemiColon
@TheSemiColon 4 года назад
the size of final vector for each word is the size of word vector.
@randomforrest9251
@randomforrest9251 3 года назад
nice slides!
@TheEducationWorldUS
@TheEducationWorldUS 4 года назад
nice explanation
@naveenkinnal5413
@naveenkinnal5413 4 года назад
Just one question. So the final word vector size is the same as sliding window size?
@TheSemiColon
@TheSemiColon 4 года назад
No, sliding window can be of any size.
@DangNguyen-xx3zi
@DangNguyen-xx3zi 3 года назад
Appreciate the work put into this video, thank you!
@TheSemiColon
@TheSemiColon 3 года назад
Glad it was helpful!
@prajitvaghmaria3669
@prajitvaghmaria3669 5 лет назад
Any idea how to create a deep learning chatbot with keras and tensorflow for WhatsApp platform using python from scratch ?
@qaisgafer3562
@qaisgafer3562 4 года назад
Great
@mohitagarwal437
@mohitagarwal437 3 года назад
Best bhai aapne pura data science kar rakha hai kya ?
@hadrianarodriguez6666
@hadrianarodriguez6666 4 года назад
Thanks for the explanation! If I want to work with terms of two tokens, how can I do it?
@TheSemiColon
@TheSemiColon 4 года назад
you may want to append them may be ?
@MultiAkshay009
@MultiAkshay009 5 лет назад
great work! 😍I am really thankful to you. But still I have a doubt with implementation part. 1) How to train the models for new datasets? 2) How to use both approaches differently CBOW and Skip-gram for training of the models? I badly need help with this. :(
@TheSemiColon
@TheSemiColon 5 лет назад
Thanks a lot. If you are implanting it from scratch then you have to encode each word of your database as a one hot vector train it using anyone of the algorithm skipgram or cbow and then pull out it's weights. Then multiply the weights with the one hot vector. The tensor flow official blog has a very nice example for it. You may use libraries like gensim to do it for you.
@jatinsharma782
@jatinsharma782 5 лет назад
Very Helpful 👍
@fahdciwan8709
@fahdciwan8709 3 года назад
what is the purpose of multiplying the 3*5 Weight Matrix with the one-hot vector of the word? How does it improve the embeddings?
@SameerKhan-ht4mx
@SameerKhan-ht4mx 2 года назад
Basically the weight matrix is the word embedding
@nazrulhassan6310
@nazrulhassan6310 3 года назад
fabulous explanation but I need to do some more digging
@Mr.AIFella
@Mr.AIFella 6 месяцев назад
The matrices multiplication not correct. I think it should be 5x1 1x3 to be equal 5x3 to be multiplied by 3x1 to equal 5x1. Right?
@KARIVENKATARAMPHD
@KARIVENKATARAMPHD 5 лет назад
nice
@josephselwan1652
@josephselwan1652 2 года назад
it took me 10 times to understand it. but i finally did. lol what we do to get a job haha
@shikharkesarwani9051
@shikharkesarwani9051 4 года назад
The weight matrix should be 5x3 (input to hidden) and 3x5 (hidden to output) @The Semicolon
@Agrover112
@Agrover112 4 года назад
Wx+b hota hai
@dhruvagarwal4477
@dhruvagarwal4477 4 года назад
What is the meaning of vector size?
@tobiascornille
@tobiascornille 3 года назад
Which matrix is the embedding matrix in CBOW? W or W' ?
@TheSemiColon
@TheSemiColon 3 года назад
it's W.
@AryanKhandal7399
@AryanKhandal7399 5 лет назад
sir aswesome
@qingyangluo7085
@qingyangluo7085 4 года назад
how to get the word embedding vector using CBOW? what neighbour words do i plug in?
@TheSemiColon
@TheSemiColon 4 года назад
You have to iterate over a corpus. Popular ones are Wikipedia, google news etc.
@qingyangluo7085
@qingyangluo7085 4 года назад
@@TheSemiColon Say I want to get the embedding vector of the word "love", this vector depends on what context/neighor words I plug in.
@imanbio
@imanbio 4 года назад
Plz fix the matrix sizes (3x5 should be 5x3 and vice versa..) - nice presentation
@vionagetricahyo1268
@vionagetricahyo1268 5 лет назад
hey can you share this code ?
@theacid1
@theacid1 3 года назад
Thank you. My prof is unable to explain it.
@_skeptik
@_skeptik Год назад
i didn't fully catch the difference between cbow and skipgram in this explanation
@saikiran-mi3jc
@saikiran-mi3jc 3 года назад
No much content in the channel to subscribe(i mean to say no playlist on nlp or cv ) .I came hear with lot of hopes. Content in the video is good.
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