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
4 жыл бұрын
Glad it helped!
@nax2kim2
3 жыл бұрын
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
6 ай бұрын
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 ❤
@fabricesimodefo8113
4 жыл бұрын
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
4 жыл бұрын
This is what I needed when I was creating it, but did not find it anywhere :)
@user-fy5go3rh8p
3 жыл бұрын
This is the best explanation I've encountered so far. Thank you!
@sheshagirigh
5 жыл бұрын
Thanks a ton. By far the best i could find after a lot of searching.. even better than few from stanford lectures!
@Amf313
2 жыл бұрын
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🙏
@thunder-v8h
3 жыл бұрын
Thank you sir! I always come back to this video when I forgot about the concept.
@subhamprasad6808
3 жыл бұрын
Finally, I understood the concept of Word2Vec after watching this video. Thank you.
@chihiroa1045
Жыл бұрын
Thank you so much! This is the most clear and organized tutorial I found on Word2Vec!
@jiexiong8522
5 ай бұрын
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!
@tylerlozano152
4 жыл бұрын
Thank you for the thorough, simple explanation.
@maqboolurrahimkhan
3 жыл бұрын
Best and easy explanation of word2vec over the internet. Keep up the good work Thanks a ton
@pushkarmandot4426
5 жыл бұрын
The best video. Explained the whole concept in a very short amount of time
@skipintro9988
3 жыл бұрын
Thanks, bro - this one is the easiest and simplest and quickest explanation on word2vec
@johncompassion9054
5 ай бұрын
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.
@rainoorosmansaputratampubo2213
3 жыл бұрын
Thank you so much. with this explanation I can understand it easier than read from books
@anindyavedant801
5 жыл бұрын
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
@ajinkyajoshi2308
2 жыл бұрын
Very well done!! Precise and to the point explanation!!
@jamesmina7258
2 ай бұрын
Thank you. I learned a lot from your video.
@mohajeramir
3 жыл бұрын
this is the best explanation I have found. thank you
@TheSemicolon
3 жыл бұрын
Glad you found it useful, do share the word 🙂
@FTLC
Жыл бұрын
Thank you so much is was so confused before watching this video ,now its clear to me
@bryancamilo5139
6 ай бұрын
Thank you, your explanation is great. Now I have understood the concept 😁
@theunknown2090
5 жыл бұрын
Hey in cobw and skip gram Method there are 3 Weight metrics Which metric is selected as d embedding matrix ? And why
@MrStudent1978
4 жыл бұрын
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
4 жыл бұрын
Glad it was helpful!
@jusjosef
3 жыл бұрын
Very simple, to the point explanation. Beautiful!
@varunjindal1520
3 жыл бұрын
This is indeed very good video. To the point and covers what I needed to know. Thank you.
@TheSemicolon
3 жыл бұрын
Glad you found it useful, do share the word 🙂
@anujlahoty8022
Жыл бұрын
Simple and eloquent explanation.
@befesa1
4 ай бұрын
Thank you! Really good explanation:)
@carlrobinson2926
5 жыл бұрын
very nice explanation, not too long, straight to the point. thanks
@nithin5238
4 жыл бұрын
Very clear explanation man.. you deserve slow claps
@satyarajadasara9000
4 жыл бұрын
Very nice video where everything was to the point! Keep posting such wonderful content!
@shikharkesarwani9051
5 жыл бұрын
The weight matrix should be 5x3 (input to hidden) and 3x5 (hidden to output) @The Semicolon
@Agrover112
4 жыл бұрын
Wx+b hota hai
@richardgruss8171
Жыл бұрын
Excellent explanation. Thanks!
@keno2055
2 жыл бұрын
Why does the hidden layer at 4:59 have 3 nodes if we only care about the 2 adjacent nodes?
@AdityaPatilR
3 жыл бұрын
If hope can set us free hope can set you free as well !! thank you for the explanation and following what you preach ;)
@ashwinrameshbabu2418
3 жыл бұрын
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?
@gouripeddivenkataasrithbha5148
4 жыл бұрын
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
4 жыл бұрын
the size of final vector for each word is the size of word vector.
@coolbowties394
4 жыл бұрын
Thanks so much for this thorough explanation!
@TheSemicolon
4 жыл бұрын
Glad it was helpful!
@aravindaraman8667
3 жыл бұрын
Amazing explanation! Thanks a lot
@bloodzitup
5 жыл бұрын
Thanks, my lecturer had this video in his references for learning word2vec
@HY-nt8nk
3 жыл бұрын
Good work! Nicely explained.
@impracticaldev
Жыл бұрын
You earned a subsciption. Good luck!
@ankursri21
4 жыл бұрын
Thank you.. very well explained in shorter time.
@juanpablo87t
2 жыл бұрын
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?
@Mr.AIFella
7 ай бұрын
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?
@fahdciwan8709
4 жыл бұрын
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
2 жыл бұрын
Basically the weight matrix is the word embedding
@mohitagarwal437
3 жыл бұрын
Best bhai aapne pura data science kar rakha hai kya ?
@hashinitheldeniya1347
3 жыл бұрын
can we cluster word phrases into groups using this word2vec technique?
@md.prantohasan9630
4 жыл бұрын
Excellent explanation in a very short time. Take
@sadeenmahbubmobin7102
4 жыл бұрын
reading material ta bujhay de amre akhn :3
@ogsconnect1312
5 жыл бұрын
I cannot say anything but excellent. Thank you
@vid_sh_itsme4340
2 ай бұрын
is hierarchical softmax used in this?
@057ahmadhilmand6
10 ай бұрын
i still dont get it, the word vector for each word is a matriks?
@romanm7530
2 жыл бұрын
Диктор просто огонь!
@absoluteanagha
3 жыл бұрын
Love this! Such a great explanation!
@Zinghere
2 жыл бұрын
Great explanation!
@Hellow_._
Жыл бұрын
how can we give all input vectors in one go to train the model?
@hardikajmani5088
4 жыл бұрын
Very well explained
@MehdiMirzapour
5 жыл бұрын
Thanks. It is really a brilliant explanation!
@nazrulhassan6310
3 жыл бұрын
fabulous explanation but I need to do some more digging
@sunjitrana374
5 жыл бұрын
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.
@hadrianarodriguez6666
4 жыл бұрын
Thanks for the explanation! If I want to work with terms of two tokens, how can I do it?
@TheSemicolon
4 жыл бұрын
you may want to append them may be ?
@imanbio
4 жыл бұрын
Plz fix the matrix sizes (3x5 should be 5x3 and vice versa..) - nice presentation
@iliasp4275
3 жыл бұрын
thank you , The Semicolon.
@mohajeramir
4 жыл бұрын
this was excellent. Thank you
@TheSemicolon
4 жыл бұрын
Glad it was helpful!
@josephselwan1652
2 жыл бұрын
it took me 10 times to understand it. but i finally did. lol what we do to get a job haha
@naveenkinnal5413
4 жыл бұрын
Just one question. So the final word vector size is the same as sliding window size?
@TheSemicolon
4 жыл бұрын
No, sliding window can be of any size.
@prajitvaghmaria3669
6 жыл бұрын
Any idea how to create a deep learning chatbot with keras and tensorflow for WhatsApp platform using python from scratch ?
@MultiAkshay009
6 жыл бұрын
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
6 жыл бұрын
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.
@theacid1
3 жыл бұрын
Thank you. My prof is unable to explain it.
@BrunoCPunto
3 жыл бұрын
Awesome
@gauharahmad2643
5 жыл бұрын
Sir what do we mean by size of each vector in 4:37 ?
@haorao2464
3 жыл бұрын
Thanks so much!
@hs_harsh
5 жыл бұрын
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!
@himanshusrihsk4302
4 жыл бұрын
Really very useful
@renessadesouza5601
3 жыл бұрын
Thank you so much
@qingyangluo7085
4 жыл бұрын
how to get the word embedding vector using CBOW? what neighbour words do i plug in?
@TheSemicolon
4 жыл бұрын
You have to iterate over a corpus. Popular ones are Wikipedia, google news etc.
@qingyangluo7085
4 жыл бұрын
@@TheSemicolon Say I want to get the embedding vector of the word "love", this vector depends on what context/neighor words I plug in.
@TheEducationWorldUS
4 жыл бұрын
nice explanation
@arnav3674
4 ай бұрын
Good !
@dhruvagarwal4477
4 жыл бұрын
What is the meaning of vector size?
@parthpatel3900
5 жыл бұрын
Wonderful video
@muhammedhassen4354
5 жыл бұрын
easy way explanation gr8
@_skeptik
Жыл бұрын
i didn't fully catch the difference between cbow and skipgram in this explanation
@prathimads2876
5 жыл бұрын
Thank you so much Sir...
@saikiran-mi3jc
3 жыл бұрын
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.
@tobiascornille
3 жыл бұрын
Which matrix is the embedding matrix in CBOW? W or W' ?
@TheSemicolon
3 жыл бұрын
it's W.
@tumul1474
5 жыл бұрын
awesome !!
@randomforrest9251
3 жыл бұрын
nice slides!
@alialsaffar6090
6 жыл бұрын
This was enlightening. Thank you!
@DangNguyen-xx3zi
3 жыл бұрын
Appreciate the work put into this video, thank you!
@TheSemicolon
3 жыл бұрын
Glad it was helpful!
@Simply-Charm
4 жыл бұрын
Thank you
@ms10596
5 жыл бұрын
So helpful
@abdallahessam4671
2 жыл бұрын
Correction, the English language has 600,000 words, only the Arabic language has this number that you mentioned is more than 12 million words
@vionagetricahyo1268
5 жыл бұрын
hey can you share this code ?
@qaisgafer3562
5 жыл бұрын
Great
@abdullahsahin4651
2 жыл бұрын
It was very confusing explanation. The slides are not consisted with the presenter's speech.
@fabricesimodefo8113
4 жыл бұрын
typo 5:25 the input words should change to "set" and "free"
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