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.

@TheSemiColon

4 жыл бұрын

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

@TheSemiColon

4 жыл бұрын

This is what I needed when I was creating it, but did not find it anywhere :)

Thanks a ton. By far the best i could find after a lot of searching.. even better than few from stanford lectures!

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!

Thank you for the thorough, simple explanation.

Thank you sir! I always come back to this video when I forgot about the concept.

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🙏

Finally, I understood the concept of Word2Vec after watching this video. Thank you.

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

Very simple, to the point explanation. Beautiful!

very nice explanation, not too long, straight to the point. thanks

Thank you so much. with this explanation I can understand it easier than read from books

Thanks, my lecturer had this video in his references for learning word2vec

Thanks, bro - this one is the easiest and simplest and quickest explanation on word2vec

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.

Very nice video where everything was to the point! Keep posting such wonderful content!

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!

The best video. Explained the whole concept in a very short amount of time

Very well done!! Precise and to the point explanation!!

Thank you. I learned a lot from your video.

Hey in cobw and skip gram Method there are 3 Weight metrics Which metric is selected as d embedding matrix ? And why

Thank you.. very well explained in shorter time.

Thanks. It is really a brilliant explanation!

Love this! Such a great explanation!

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.

Awesome explanation. Thanks!

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 🙂

Simple and eloquent explanation.

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?

Thank you, your explanation is great. Now I have understood the concept 😁

Thank you! Really good explanation:)

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

Thank you so much is was so confused before watching this video ,now its clear to me

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!

Very clear explanation man.. you deserve slow claps

Thanks so much for this thorough explanation!

@TheSemiColon

4 жыл бұрын

Glad it was helpful!

Thank you so much Sir...

this was such an informative lecture, thank you.

I cannot say anything but excellent. Thank you

this is the best explanation I have found. thank you

@TheSemiColon

3 жыл бұрын

Glad you found it useful, do share the word 🙂

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?

Amazing explanation! Thanks a lot

Диктор просто огонь!

Good work! Nicely explained.

If hope can set us free hope can set you free as well !! thank you for the explanation and following what you preach ;)

awesome !!

This was enlightening. Thank you!

Sir what do we mean by size of each vector in 4:37 ?

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.

can we cluster word phrases into groups using this word2vec technique?

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

5 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.

You earned a subsciption. Good luck!

Wonderful video

Excellent explanation in a very short time. Take

@sadeenmahbubmobin7102

4 жыл бұрын

reading material ta bujhay de amre akhn :3

Thanks a lot!

Great explanation!

Best bhai aapne pura data science kar rakha hai kya ?

this was excellent. Thank you

@TheSemiColon

4 жыл бұрын

Glad it was helpful!

Thanks so much!

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 ?

easy way explanation gr8

Why does the hidden layer at 4:59 have 3 nodes if we only care about the 2 adjacent nodes?

Very well explained

So helpful

thank you very much

how can we give all input vectors in one go to train the model?

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

Really very useful

Any idea how to create a deep learning chatbot with keras and tensorflow for WhatsApp platform using python from scratch ?

thank you , The Semicolon.

Thank you

Thank you so much

The weight matrix should be 5x3 (input to hidden) and 3x5 (hidden to output) @The Semicolon

@Agrover112

4 жыл бұрын

Wx+b hota hai

fabulous explanation but I need to do some more digging

i still dont get it, the word vector for each word is a matriks?

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.

Plz fix the matrix sizes (3x5 should be 5x3 and vice versa..) - nice presentation

What is the meaning of vector size?

Awesome

Good !

hey can you share this code ?

nice explanation

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.

it took me 10 times to understand it. but i finally did. lol what we do to get a job haha

Great

nice

Which matrix is the embedding matrix in CBOW? W or W' ?

@TheSemiColon

3 жыл бұрын

it's W.

nice slides!

Thank you. My prof is unable to explain it.

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?

sir aswesome

Very Helpful 👍

Appreciate the work put into this video, thank you!

@TheSemiColon

3 жыл бұрын

Glad it was helpful!

i didn't fully catch the difference between cbow and skipgram in this explanation

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.

typo 5:25 the input words should change to "set" and "free"