Do you want to learn technology from me? Check codebasics.io/ for my affordable video courses.

I started searching wordtovec videos after failing to understand it by following NG's lessons. That is the single video that can actually tell that the word embeddings are 'the side effects' of the training process and this is how it finally clicked for me. Thank you very much!

@anubhavthakur2985

2 ай бұрын

then you didn't searchthe youtube enough

As part of my NLP dissertation, I was looking for some real time use cases with some clear explanation. I found this a super useful and thank you for great demonstration with so many examples which are easy to understand. You rock with your teaching skills!!

@girmayohannis4659

2 ай бұрын

nice to meet you here, in which university are you studying for your PhD?Thanks

Presenting complex understand matter in an simplified way Dhaval Sir we you are an patience,consistent, simplified ,organised way of subject presentation expert.Basics-Theroy-Coding-Pratice..with..Great Explaination.

This is the video that finally helped me grasp this concept. Thank You!

Great explanation .. 🙌🙌🙌 After watching many videos on this topic finally my understanding is cristal clear. You are doing awesome job sir.

@codebasics

3 жыл бұрын

I appreciate you leaving a comment of appreciation

So clear, so eloquent, and so concise. Your contents are gift to this world. Thank you for using your intelligence, diligence and teaching skills to make a positive mark.

@fidaeharchli4590

3 ай бұрын

i confirm

I like how you love your homeland and use it in all examples. Greetings and Love from Azerbaijan.

Great Visual way of teaching! Thank you so much Sir ❤️

One of the best videos on word2vec

Awesome explanation. Crystal Clear.

Crystal clear explanation!! Thanks you so much sir

Awesome explanation of the concept!

Thank you! The explanation was very clear.

incredible content. this guy is one of the best on youtube

@codebasics

2 жыл бұрын

I appreciate you leaving a comment of appreciation

great teacher, great explanation, great presentation, great context

@fidaeharchli4590

3 ай бұрын

thank you

Great explanation! Thank you very much

Thanks for this awesome tutorial waiting for coding part :)

Excited 😄

This was a useful introduction, I don't have the math chops to understand it, but it was useful to hear some of these definitions.

Easy explanation!! Tks much👍👍

Mind blowing 🤯🤯 Thank you!

Amazing Video 👏🌟. Thank you so much for the great explanation

Fantastic explanation!

Thank you Sir for this playlist

A Very good explaination - really very helpful

superbly explained !!

thanks a lot...holly great..pls complete the playlist asap

Very good explanation. Thanks.

Well done. This is one of the best course on word2vec so far. I do have a master degree in AI and event that I did not work professionaly in the field your cour brough a lot of souvenirs haaa.. During my master 15 years ago I introduced an archaich method for resolving question/answering based on linkgramar, wordnet, verbnet and semnet. At the end of my syntactical analysis I also discovered that by just using world context it was possible to comme up with a vector representation of named entities.. The innovation is here is the use of neural network to give a value to the world. This is just brilliant. In my thesis I was already showing that language is just a code representing a subjective version of one universe and that human and animal comunicate using theirs own code.

Also what would be your next topic in deep learning, is it sequence to sequence models?

Super explanation ..Thank you so much

Awesome man...loved it...can you pls upload some code walk through of this concept -- some gud projects

Great explanation!

Good Explanation Sir.Thank you

Great explanation as always

Very useful and informative

you are the real teacher.....what should i say for you ???? thank you sir...thank you so much.........

Great explanation

Wow, very very clear . Thank you 🙏

@codebasics

3 жыл бұрын

Glad it was helpful!

That was a great explanation. Thanks. I have this one question in my mind. If all words in documents are unique then how word2vec will find vector for the last 2 words? Considering cbow

I think Dhaval, there is no non-linear activation function between the input layer and hidden layer. Correct me if I am wrong.

I was watching Andrew Ng's course on sequence models and his lecture on word2vec is just a bullshit. Thanks god I found your video, amazing explanation.

Beautiful video

would you please create a playlist on NLP?

Will be get nearly identical word vectors for CBOW and skim gram methods for a particular word say 'king'?

Hello sir, Please make a video on GRE and IELTS preparation , this will be more useful and helpful to students like me planning to study Masters Abroad as your videos are clear, we get motivated . Thank you.

Awesome thank you

Approximately how many videos are going to come in this series except the existing videos, by the way thanks a lot sir, the only playlist on youtube which was way more knowledgeable for machine learning and deep learning..

@codebasics

3 жыл бұрын

There will be atleast 5 to 10 videos coming up and then I will start the project series

Sir your video is awesome 🙌,i have one doubt ,what is the main difference between skip gram and bag of words model?

There's a subtle mistake in your CBOW explanation at 8:34 . In CBOW the target is always the central word based on context i.e the surrounding word . That means for a substring "Emperor ordered his" and window size of 3 the target is "ordered" and features are "Emperor , this"

@ashwinshetgaonkar6329

2 жыл бұрын

so he explained skip gram

@ashwinshetgaonkar6329

2 жыл бұрын

so he explained skip gram

@abir95571

2 жыл бұрын

@@ashwinshetgaonkar6329 yes

superb..

Thanks a lot 😌

Superb

Thank you.

thank you so much

Amazing

you are the best

The king-man+woman=queen equation tells me that we are not embedding words into a vector space but into an affine space which is like a vector space but where we do not have a notion of a zero vector. Perhaps we can obtain a zero vector simply by taking the weighted average over all words or by doing some regularization during training so that we naturally get a zero vector. What will the zero vector mean anyways?

Great explained finally

@codebasics

2 жыл бұрын

🙏🙏

thanx we are learning a lot from you

@codebasics

3 жыл бұрын

Glad it was helpful!

@notknown9307

3 жыл бұрын

@@codebasics waiting for your next upload you are doing your work very well👍👍

He played it really well when he marked male = -1

Thanks a lot for explaining this using a neural network diagram :)

@codebasics

3 жыл бұрын

🙂👍

@rahulsoni412

3 жыл бұрын

@@codebasics can you explain how the number of weights are calculated in word embedding, I mean the number of total weights. I was getting confused while calculating the number of weights.

Where do we get the predicted output from? How do we enter it for comparison?

Thanks sir

Brilliant

is there standart real list for every onject given here. For example for cats, tails 0.2?

Hi Dhaval, Great video on W2V, The link for the coding part of implementing Word2Vec in Python, please?

@codebasics

3 жыл бұрын

Yes that video is coming up soon. I have not yet uploaded it

What a mathematician would do when he/she hear you say "a vector is nothing but a set of numbers"

Can you say about cyber security scopes skills

Hello I have doubt in this explanation, aren't all the weights gonna be same when our neural network is trained ? what I mean is once we train a network W(T)X is what triggers a output node so how do we have different weights for every output word

@cherupawan3777

Жыл бұрын

Did u got answer to this

Great Explanation :) Crisp and to the point , Better than Hrithik Roshan Super Hero Movie's Explanation :P :P

Is word2vec using dimensional reduction too?

bam! life saver

7:20 Meaning of word can be inferred by surrounding words

Hi, it is a wonderful explanation for word2vec I've ever seen.I have a question,I have my own corpus and I have built multiple wor2vec models, How to evaluate these models and how am I gonna choose the best one???

@codebasics

Жыл бұрын

One approach is to take a classification or some other NLP problem in your domain and build NLP classification model using your embeddings. You can then check the performance of those models to evaluate how effective embeddings are

@uwaisahamedimad556

Жыл бұрын

@@codebasics thanks a lot for the reply. based on your answer it seems like there is no standard or at least a well-established evaluation method for the performance of word embeddings.

We need a course about NLP Transformers..

sir, how do u unzip the json file using git bash , is not clear to me. help me plz. thanks.

wheere is neural network link?

The more you dig deeper into a thing, the greater the tutor gets

How to evaluate a word2vector model

I think there's a mistake , because in both cbow and skip gram , the weights that made the embdeddings are always between input and hidden layer , and here in cbow you mentioned the weights between hidden and output are considered.

@amanagrawal4198

6 күн бұрын

CBOW Model Architecture Review The CBOW architecture works by predicting a target word based on the context words around it. Here’s a step-by-step explanation of the flow: Input Layer: This consists of several one-hot encoded vectors corresponding to the context words. Projection Layer (Hidden Layer): Each one-hot vector is used to retrieve a word embedding from the first weight matrix (input-to-hidden weights). Unlike typical neural networks, there is no activation function here; the embeddings are simply summed or averaged (depending on the implementation) to produce a single dense vector. This vector represents the combined semantic content of the context words. Output Layer: The averaged embedding vector is then projected to the output layer using a second set of weights (hidden-to-output weights). The output layer is a softmax layer that predicts the probability distribution over the vocabulary for the target word. Role of Weights in Embedding Formation Input-to-Hidden Weights: This is essentially the embedding matrix. Each row in this matrix corresponds to the embedding of a word in the vocabulary. When context words are fed into the model, their embeddings are retrieved by indexing this matrix with the one-hot vectors. These embeddings are what you typically extract and use as pre-trained embeddings for other tasks. Hidden-to-Output Weights: These weights are used to transform the combined embedding from the hidden layer into a prediction for the target word. Each column in this matrix (since it's typically the transpose of the embedding matrix in many implementations) can be seen as a "contextual embedding" of a word when it acts as a target.

I think you mean 'side products' rather than 'side effect'?

The statement "King - man + woman = Queen" is well-known in machine learning. However, when we examine the characteristics of a king, they often include being super rich, having authority, and possibly not having a tail. Yet, there is a contradiction: a lion is also referred to as a king, and it does have a tail. How can a computer differentiate between a human king and an animal king? Doesn't this introduce bias since the training corpus typically associates "king" with humans rather than animals? Just because something appears less frequently or is absent from the corpus doesn't mean it lacks value or significance.

doesn't CBOW mean Contextual Bag of Words?

@codebasics

2 жыл бұрын

Continuous Bag Of Words: analyticsindiamag.com/the-continuous-bag-of-words-cbow-model-in-nlp-hands-on-implementation-with-codes/

@houchj0372

2 жыл бұрын

@@codebasics you are correct, thank you. By the way, this video is excellent.

Can I get an admission in bsc data science after 12th commerce in St Xavier's College Mumbai ???? and I've mathmatics in optional subject ??? please please please please please please please please please tell me I've been requesting you for 6 months 🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏

working?

King of the jungle has tail though

3:35 paygap lmao

@santoshsaklani5019

2 жыл бұрын

Kindly make video on vulnerability prediction using wordtovec

Lion King has tail 😅

the thing is your all videos are connected to previous I am unable to watch a whole video you always made me pause and watch a previous video that's really a problem first i was watching the text classification video you said go watch bert first then in that video you said go watch word2vec then you said go watch part 1 first then now in this video you said go watch neural network now tell do you really want me to watch a whole video because i am just opening a new tab repitively.

found a better explanation here - kzread.info/dash/bejne/fKGZxMOaoKTJe84.html

3:56 why horse and woman are same gender for start ????? then king minus men is gender -2 adding a woman or horse to that you get gender -1 which is men or king !?????

@priyeshsrivastava8025

2 жыл бұрын

no its (-1) - (-1) + (+1) = +1 i.e. queen

but in case of king of jungle that is lion, he has a tail,😃 just saying...