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 ❤
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 :)
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.
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
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!
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🙏
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.
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.
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....
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
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?
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. :(
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.
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.
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!
![[Classic] Word2Vec: Distributed Representations of Words and Phrases and their Compositionality](http://i.ytimg.com/vi/yexR53My2O4/mqdefault.jpg)
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 ❤
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 :)
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.
Glad it helped!
This is the best explanation I've encountered so far. Thank you!
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
This is what I needed when I was creating it, but did not find it anywhere :)
Finally, I understood the concept of Word2Vec after watching this video. Thank you.
Thank you sir! I always come back to this video when I forgot about the concept.
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!
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🙏
Thanks a ton. By far the best i could find after a lot of searching.. even better than few from stanford lectures!
Thank you so much! This is the most clear and organized tutorial I found on Word2Vec!
Best and easy explanation of word2vec over the internet. Keep up the good work
Thanks a ton
Thanks, bro - this one is the easiest and simplest and quickest explanation on word2vec
Thank you for the thorough, simple explanation.
Simple and eloquent explanation.
Thank you so much. with this explanation I can understand it easier than read from books
The best video. Explained the whole concept in a very short amount of time
Best Explanation so far mate :) Keep up the good work!
Thank you so much
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.
this is the best explanation I have found. thank you
Glad you found it useful, do share the word 🙂
Very clear explanation man.. you deserve slow claps
Thank you so much is was so confused before watching this video ,now its clear to me
Very well done!! Precise and to the point explanation!!
Very simple, to the point explanation. Beautiful!
Why does the hidden layer at 4:59 have 3 nodes if we only care about the 2 adjacent nodes?
Thank you, your explanation is great. Now I have understood the concept 😁
Great explanation!
very nice explanation, not too long, straight to the point. thanks
The weight matrix should be 5x3 (input to hidden) and 3x5 (hidden to output) @The Semicolon
Wx+b hota hai
Good work! Nicely explained.
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.
the size of final vector for each word is the size of word vector.
Amazing explanation! Thanks a lot
Диктор просто огонь!
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....
Glad it was helpful!
Thank you. I learned a lot from your video.
Thanks, my lecturer had this video in his references for learning word2vec
If hope can set us free hope can set you free as well !! thank you for the explanation and following what you preach ;)
This is indeed very good video. To the point and covers what I needed to know. Thank you.
Glad you found it useful, do share the word 🙂
Thanks so much for this thorough explanation!
Glad it was helpful!
Hey in cobw and skip gram
Method there are 3
Weight metrics
Which metric is selected as d embedding matrix ? And why
is hierarchical softmax used in this?
Thank you! Really good explanation:)
Very well explained
I cannot say anything but excellent. Thank you
Awesome explanation. Thanks!
Thank you.. very well explained in shorter time.
Love this! Such a great explanation!
Very nice video where everything was to the point! Keep posting such wonderful content!
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?
how can we give all input vectors in one go to train the model?
i still dont get it, the word vector for each word is a matriks?
Excellent explanation in a very short time. Take
reading material ta bujhay de amre akhn :3
Thanks. It is really a brilliant explanation!
You earned a subsciption. Good luck!
can we cluster word phrases into groups using this word2vec technique?
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?
Basically the weight matrix is the word embedding
thank you , The Semicolon.
Insightful!
how to get the word embedding vector using CBOW? what neighbour words do i plug in?
You have to iterate over a corpus. Popular ones are Wikipedia, google news etc.
@@TheSemicolon Say I want to get the embedding vector of the word "love", this vector depends on what context/neighor words I plug in.
Which matrix is the embedding matrix in CBOW? W or W' ?
it's W.
Thanks for the explanation! If I want to work with terms of two tokens, how can I do it?
you may want to append them may be ?
Sir what do we mean by size of each vector in 4:37 ?
Just one question. So the final word vector size is the same as sliding window size?
No, sliding window can be of any size.
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
Wonderful video
this was excellent. Thank you
Glad it was helpful!
fabulous explanation but I need to do some more digging
Best bhai aapne pura data science kar rakha hai kya ?
Plz fix the matrix sizes (3x5 should be 5x3 and vice versa..) - nice presentation
Really very useful
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?
easy way explanation gr8
Thank you so much Sir...
nice explanation
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?
What is the meaning of vector size?
This was enlightening. Thank you!
Appreciate the work put into this video, thank you!
Glad it was helpful!
thank you very much
awesome !!
Thanks a lot!
it took me 10 times to understand it. but i finally did. lol
what we do to get a job haha
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. :(
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.
So helpful
nice slides!
Awesome
Thank you
Thank you. My prof is unable to explain it.
hey can you share this code ?
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.
Any idea how to create a deep learning chatbot with keras and tensorflow for WhatsApp platform using python from scratch ?
Good !
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!
typo 5:25 the input words should change to "set" and "free"
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.
Very Helpful 👍
Correction, the English language has 600,000 words, only the Arabic language has this number that you mentioned is more than 12 million words
Great
i didn't fully catch the difference between cbow and skipgram in this explanation
nice