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Word Embeddings
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- เผยแพร่เมื่อ 14 ก.ค. 2017
- Word embeddings are one of the coolest things you can do with Machine Learning right now.
Try the web app: embeddings.macheads101.com
Word2vec paper: arxiv.org/abs/1301.3781
GloVe paper: nlp.stanford.edu/pubs/glove.pdf
GloVe webpage: nlp.stanford.edu/projects/glove/
Other resources:
www.aclweb.org/anthology/Q15-1016
en.wikipedia.org/wiki/Word_em...
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This is so relevant in 2023, timeless explanation!
You spent considerable no of minutes to explain the nature of the problem before presenting the solution to the problem - I like that approach.
You're a genius. I was struggling to understand the word embedding concept through Stanford University videos. Your explanation and pace is much better. Thanks. Keep making such videos.
I honestly think this is a really good explanation to word embedding. Breaks the nitty gritty involved in word2vec and co-occurrence. I’ve read a couple article and watched a few videos but by far yours is the easiest to comprehend. Thank you so much.
Cheers from Malaysia!
An Exceptional Lecture! What I loved about it is its focus on implementation and results which really helps build good intution around it, to garner interest and dig deeper into math details later which most us are boggled before knowing how powerful/useful the results are. Thank you again.
Dude, seriously, thank you. I am not a CS major but I got absurdly interested in AI during my last years of college. It's inspiring to see your self-learnt enthusiasm and it gives me strength to follow my interest. Keep up the good work and thank you!
Cheers from Buenos Aires, Argentina
cc
You are very good at explaining pretty complex concepts. I appreciate the time you took to do this, it was very informative.
I've gone through several embedding explanations until I arrived at this one. Well done, finally all pieces make sense. Thanks!
Glad to see a relatively new video from you guys. I'm an old-time fan. And this is exactly what I'm attempting to learn right now.
This is really well explained. Best word2vec explanation ive seen on youtube so far.
Glad to have come across this while looking for materials to learn about word embeddings to understand how text prompts work in stable diffusion text to image models in 2023. You're a great teacher. A lot of videos on this topic across yt is full of jargons without clear explanation. in 2023 there's still tons of relevance of this video with where the current state of technology is
I recently started learning NLP, and the part of word embeddings was just not clear at all. It makes sense now.
Thank you a lot ! You won a subscriber
Probably the most clear explanation of word2vec. Thanks for the video.
amazing man... i was going through 10's and 10's of online respurces to understand what is word embeddings! They way you explained it made me directly subscribe to your channel... very well. thank you very much
Excellent work. I was looking for such an explanation on word embeddings & I am happy that I found this. Thanks.
Humble, concise, brilliant... Subscribed!
Thank you so much for your video! It's the best learning material I can find on youtube.
one word, awesome. Thanks a lot for the video.
Humble, concise, brilliant... Subscribed!
Why have you stopped making videos man. Miss your vids
For someone who knows nothing and wants to know everything in ML, this is the simply awesome explanation.
Awesome explanation , after hours of looking for good stuff to understand the word embeddings. Thanks !
i really appreciated the emphasis you gave on word embeddings rather than on word2vec.
I liked the way you explained...crisp and clear!
True genius knows how to explain a complex concept in a really simple and intuitive way. Solut.
good lecture, includes important information like what he finds cool, that it’s the best example what can be done etc. that allows the listener to organize information hierarchically. not like some university lectures, where just a monolithic dump of text is flowing out and we don’t know what is important to remember and what not.
Awesome video man. Extremely well presented. I'm impressed with your presentation skills. thanks for the video
I haven't seen a better explanation on this. Thank you. This was really good.
Just starting out to venture into this world. Thanks for the explanation. I have a medical background, but no background on computer science. So this gives me a little bit hope in learning totally something new.
Great video. I was having difficulty understand 'context'. You explained clearly. Thank you.
this is really intuitive. most places I got answers like representing words with vectors which wasn't helpful. now i think i have a proper idea.
That was an amazing explanation. Thank you!
Your explanation was very clear and simple, thanks for making this video.
More generally, word2vec is nothing more than graph2vec. Sentences can be seen as random walks in the english-language graph, in which each word is a node and every world is connected to other words. The strength of this connections depend on how frequent they appear on the same context. Seen this as graphs allow you to run network analysis and see what other kinds of information you can extract from it. By doing it right, you might even be able to estimate the connections for words that didn't appear in the training set and try to update the model to make it better.
Or use the word embedding and try to embed sentences and see how that goes.
I just started learning ML concepts, and this video helped tremendously with word embeddings. You got another subscriber. I do have a question though. In your example, how did you decide on 64 as the other dimension of your factored matrices? Would a larger number there give you a better word embedding?
Fun fact: This guy went on to work at OpenAI and is one of the creators of DALL-E 2.
Haha nice, as soon as I saw him speak for 5 seconds, and saw the timestamp of 5 years ago. I typed this -"If this guy isn't already a founder of a leading company in this AI wave, i'll be disappointed. but hey most of the smartest people don't always see success. And fame, money isn't everything."
Glad to see I was right haha. I was like there is a high probability, considering this was 5 years ago, and if nothing else in the Universe interfered with this guy's life trajectory just based on the way this guy talks, and looks which basically shout young and motivated, or hungry should mean he is one of the ls a big time guy now.
no wonder a man like him can do the job...
Amazing man. Thank you for your time!
HI Macheads, I have ran into so many hopelessly disappointing video presenters or live 'trainer" who just talk to themselves. Evenworse they make me confused about areas i already know and make you even confused about things you know. haha (even from the top IT corporations). Hope you can talk in our upcoming training session - just to demonstrate what is a quality presentation of tech ideas. You have inborn ability to explain and motivate
Brilliant explanation! Thank you!
Oh Man u are excellent in explaining word2vec, hoping to see some more in text NLP.
Informative, interesting. Raised my interest in the topic of word embedding
This was brilliant. Keep on creating.
Great explanation!! Thanks a lot
Awesome explanation, thanks.
8:08 Thank you! This explained the missing piece to me. Multiple other videos on that topic where missing this nice and easy to understand diagram.
Thanks for sharing! It helped a lot!
Man you are my teacher from now onwards.
Great explanation!
Awesome recap !
Beautiful, great work
I really appreciate this video sir !
I'm wondering if something similar to Disentangled Variational Autoencoding could be used to improve a word2vec embedding. I'm not quite sure on the details, but it seems DVA has an effect similar to stuff like factor analysis, and principal component analysis, producing a latent space whose dimensions are more akin to real "separate" dimensions. Aka, producing separate dimensions for the italic-ness and boldness of a written digit, as seen in the paper Disentangled Variational Auto-Encoder for Semi-supervised Learning by Yang Li et al. (I would link it but TH-cam has a history of assuming comments with links in them are spam.)
If that technique translates well into word vectors, it could for example result in a model where "maleness" is its own dimensions. i.e. "man" - "woman" ~= "king" - "queen" ~= (0, 0, 0, 1, 0, 0, ...) (A large vector that is parallel to one of the dimensions.)
Another interesting venture would be to pre-process the data using an NLP library, so that different forms of the same lemma are already grouped together by default, and so that homographs can be separated. It could also expose information that a skip-gram or bag-of-words model would miss, such as dependency / sentence structure.
I really ought to get my hands dirty some time instead of just thinking about this stuff in my head...
Brilliant video! Thanks :)
I just learning embedding layers and luckily I ran into this video.
Very nice explanation, thanks
very well explained word2vec Vs glove, thank you.
Thanks man ! good explanation
I'm glad you made this video. Now I don't have to download the vector for every word on twitter.
hello, friend. I need word file that was available on this side.Now the link is not working. Help me and send me the words vector file.Please
link : embeddings.macheads101.com/
My Email : sufyan.ali7272@gmail.com
This is gold! thank you (from the future :-))
please i need some help with a mac .... apear on my screen this leyend ..... SystemExtr se ha cerrado inesperadamente.
Tx so much
Great explanation!!!
Amazing explanantion brother thank you
how could embedding be applied to other field?
Embeding is applicable to sparse features？
Thanks for the video. I have seen GloVe models that contain the stop words and basically it means that at least they do not remove stop words. I assume one can remove tham and create two different model or vectors. If so, I assume there is not that much space to talk about precision of the two approach. Right?
The other thing is that in case of embedding we do not apply stemming or other similar techniques, since the process is largely on context level. Right?
The website is really cool🔥
Great Job!
I wish I could understand word embeddings just as well as you, im still a begginer as for now but this is what I want to become. Also I share your love for WE specially because I want to develop a NLP or a language model that generates answers but its too ambitious at the moment
Just checked out the web app and was wondering how you put that together? Django? It is brilliant!
So far I was able to only use normal dtm tfidf to compare speeches given at un - using text2vec in R. Not sure if I used it correctly though. It has an option for glove too, which I want to try out because I am a beginner and started in R. Python is soo confusing for me and do not let me start on gensin packages and docs.
Great Explanation!
Hope you are doing good, I'm interested doing this thing in Urdu language. I wish you contribute with me to do this. thanks
Am a regular follower.. Keep up!!
subscribed after 1 minute!
still in 2022 much clearer explanation than my professor on my master.
Temperature: 5 years ago
Feels like 15 years ago
Why have you stopped making videos man. Miss your vids
Hi it is indeed great...
you mentioned downloading twitter data.
Is it possible that I could get this data?
Working on a similar project of sentiment analysis
great video!
Thank you! What else can i do to learn and engage?
u are doing a very good thing buddy keep it up😊😊
The app is still up, impressive.
I don't get, why all the dislikes, the explanation is great.
amazing video bro
very well explained imo
Thank you very much. How can we decide number of features in embedding matrix? When you deal with colors you have 3 features (R,G,B) but how can i define features for a 10000 words corpus? Thanks in advance.
Very helpful!!!
Hello, do you invested time on learning all Calculus things like doing some partial derivative by hand or you can abstract this? I'm kind of slipping when I see mathematical content... but I'm able to build a CNN on Tensorflow without problems. Are this painfull way?
thank you!
But i am confused about weights matrix W from where and how do we acquire these weights W and W'.
That was pretty awesome. It would be nice to see some code.
This is much better explanation than that available on some of the paid courses.
How to host the algorithm or model ..??
where i can read app source code.
nice video, thanks
Wow Great Video.
If i understood well. You have to multiply the first weight matrix (between input layer and hidden layer) with all word vector (composed of 0 and 1) to obtain the embedding matrix ? (weight matrix obtained with BackWard Propagation etc... ?)
Thanks :)
is it possible to port a mac app to swift? and can you do it?
Im new in AI, but impressed by what happens now and this hype around NLP and deep learning. Im self learning about all this. I really like what you have done, downloading tweets and word embedding. Is it possuble to access to your source code
Tks
omg you look kinda like hiccup from how to train your dragon xD also thank you so much you literally just saved my life
what hardware do you have to train these models..
When are your new videos coming about ml
Hey how long did it take to download all that twitter data? Also, did you run it in the cloud or directly on your mac and if you ran it in the cloud, what service provider did u use? Thanks for your videos
thanks