Word2Vec (introduce and tensorflow implementation)

explain what is word encoding, embedding and how word2vec provide vector representation with similarity.
code is available at
github.com/minsuk-heo/python_...
all machine learning youtube videos from me,
• Machine Learning

[@jungshin3262]

Crystal clear and brief to the point. Well done.

[@MaryMoo-lm1ry]

Thank you for the video and the explanation :) Just one note: In the 21th century, it would be very nice to finally find other examples as those, which we have trained and educated by for centuries. its so demotivating to see/hear/read again this same assignments man-brave and woman-beautiful like in the more then 100 years old fairy tales. lets go for better examples, which are inclusive and lets everybody participate equally! :) thank you!

[@Yuki-lt3wr]

Thanks to this video I finally figured it out! This is definitely one of the best videos in which Word2Vec is intuitively explained with a simple example.

[@bhairajagautam3505]

Are you kidding me ? I tried to learn from Andrew Ng, but could not grasp what he said, you on other hand made this look so fucking easy. OMG! Thank you!!!

[@kyoosikkim749]

I've watched other videos of his, and he is so good at teaching. He's always explaining "WHY" with the simplest words. Also, very concise examples. Having watched thousands of videos teaching data science, he is the best ever and I wish he would keep it going. Thank you!

[@oguzozbay8056]

Among the videos I've watched so far, this was the one that explained the topic the best.

[@meetvora6480]

A great video and wonderful explanation. Just 1 change I would like to suggest: While you are converting word to label, you are using the following line for neighbor in sentence[max(idx - WINDOW_SIZE, 0) : min(idx + WINDOW_SIZE ,len(sentence))+1] : here instead of adding +1 to the end of min() function, you should add it to only min()'s first argument only. what i mean is for neighbor in sentence[max(idx - WINDOW_SIZE, 0) : min(idx + WINDOW_SIZE+1, len(sentence))] : your method does not raise error, as python will look only till last index and ignore number higher than that. But the suggested method wont raise error incase if someone tries to access it by index rather than its value.

[@gowthamanv8740]

Awesome explanation. Do more videos about NLP and deep learning

[@kushalneo]

Simple, Smooth, and Crystal Clear. Great Work!

[@IMateme]

The first time I have a great understanding Never been simpler like this

[@VijayBhaskarSingh]

Simple and clear! great work Minsuk!

[@sourabmangrulkar9105]

A very simple to understand video with a working example - just what I wanted. Thank you :)

[@allcode4042]

Finally a good and clear explanation on what word2vec is. Thank you.

[@abhishekaggarwal9774]

Haven't seen any tutorial video better than this.. What a nice and crisp explanation :) Big thank u Minsuk :)

[@hamu4671]

Thank you so much, after those bad word2vec lectures and tutorial video This is like a revelation come to me, what a great explanation

[@itshgirish]

Wow, after digging thru so many videos i just hit my jackpot

[@yangwang9688]

A great introduction and detailed explanation to word2vec!

[@htv6530]

Giving so much information under 10 minutes : Awesome!!!

[@abh7844]

Best, concise and clear explanation on the subject. Well done.

[@reb5642]

i take hour to hour to read many article and watch many videos but still not get clear. but your 10 min videos help me figure out everything's. thanks you so much sir.

[@AjinkyaBobadepictpune]

wow, thank you ! really appreciate your efforts to make these tutorials

[@diptinemade8977]

Awesome explanation and visuals. Thanks for taking the time to create them. Cant wait to check out rest of the videos

[@TOpCoder100]

Please make an explanation on more advanced topics in NLP (e.g t-SNE). The explanation is one of the best on the KZbin. Without abstract concepts. Straight to the point. Love that guy =)

[@mohamedamrouane9730]

Thanks, it was very helpful to understand after viewing lots of videos and tutorials.

[@kamaldhungana6677]

Your explanation and demo are awesome!

[@jagan4269]

One of the best explanation i ever had. Bulls Eye. Thanks for this video

[@Dragoon77]

Thank you, nice explanation and presentation material

[@jiaoisjiao]

the best introduction of word2vec!

[@user-lt6it2iw3o]

This is so well explained. Thank you so much!

[@lukasalt5196]

Thank you for the great explanation! This really helps my with preparing for an exam

[@adityarane5900]

Excellent video ... Very simple and clear demonstration !!!

[@bassimeledath2224]

This was fantastic! Finally understood it.

[@astuscore8609]

I just start learning about word2Vec I feel like I'm already familiar with the subject haha Thank you Minsuk

[@Arif-005]

You nailed it man... Great work.. And thanks 👍

[@kushalgandhi3520]

Simple, Easy and to the point!!! Thnx a lot!!!

[@user-bm9oy4gx2l]

Thank you so much, now I cleary know what embedding is!!

[@dinoeld3800]

That's an excellent explanation. Keep up the good work!

[@sanjaykrish8719]

Beautiful Explanation.. Thanks

[@weactweimpactcharityassoci3964]

This was a very good Video, Well done Sir

[@MukeshArethia]

Simple yet very powerful explanation!!! :) thank you so much for video

[@newcoder7166]

Excellent work! Thanks.

[@manuelherrerahipnotista8586]

Extraordinary clear. Excelent video!

[@vivekvyas7438]

Very simple and best explanation

[@kvreddy1985]

Clear explanation and good example.

[@marcelomuzilli9172]

Amazing!!! Well explained.

[@rahulvaish3034]

Thanks for posting this amazing tutorial video :)

[@keichan0505]

Very clear... perfect explaination. :)

[@dvm509]

GodLike Explanation

[@justrelaxyouself294]

Good explanation!! Thank you very much!!

[@shijiezhao3681]

Thank you very much sir! Great explanation.

[@jacobyoo6886]

이해가 잘 됩니다. 고맙습니다.

[@gopinathv3172]

Nice Explanation...Thank You

[@tad.tesfaye]

awesome job, this is the best explanation I've seen so far! Can you make a video to other word embedding methods like glove, wordrank and word2gauss, please?

[@albertlee5312]

Thank you for the inspiring lecture!

[@gurmukhsingh5758]

amazingly explained!!!!!👌👌

[@aoihana1042]

Great explanation, sir!

[@apristen]

Thank you for so clear explanation!

[@sivainfotech]

Nicely explained

[@saikumarnaik1298]

wow... simply amazing

[@hoomanjafari4633]

Thank you so much, insanely helpful !

[@pushkarmandot4426]

Thanks for explaining very clearly

[@user-or7ji5hv8y]

Great video! Really clear!

[@zhengtingli4745]

Very impressive! Thanks

[@prabalghosh2954]

Thank you for the great explanation!

[@javisebuaotia5281]

정말 감사하다, 잘 설명했어요

[@mohajeramir]

Really appreciate it. It was very helpful. Thank you

[@owen1226]

the best tutorial found

[@anghoanghai4715]

Very helpful, thank you so much!

[@franklim9613]

Thanks. Now I know what is Word2Vec

[@raghuwanshraj4542]

that was such a nice explanation ,thanks a lot

[@tariqnahmad]

Thanks ssoo much. A fantastic explanation.

[@user-de7mb2tn9n]

정말 잘 설명해주시네요 ! 구독 누르고 갑니당

[@weihai1shen743]

Crystal clear!

[@izhar109]

Very well explained, thx

[@roosboom]

Well done!

[@anandsingh1011]

Awesome.. clear explanation.

[@xiangyumeng2623]

Your video is amazing!

[@bigbang7348]

best explanation........Thank You

[@okbabouilouta3340]

man!!! so clearrrrr

[@kusali11]

Very nice, thanks!

[@user-or7ji5hv8y]

Very succinct and clear.

[@hhj989]

Well done

[@harshaldeore]

Best explanation. Wish the implementation was done with keras.

[@sinanlin7471]

NICE tutorial!

[@tamago9760]

参考になります！

[@phdabel]

excelent video

[@yousefalattar5426]

جزاك الله كل خير علي هذا العمل

[@AakashOnKeys]

Thanks please keep posting!!

[@TheEasyoung]

PROJECT Black & White thanks!

[@mr_amit_bhat]

Thank you!

[@nikosnikos1825]

you are just the best

[@dankman7603]

excellent and clear, thank you

[@annajoseph68]

very effective video :D

[@gsuri1992]

Thank you so much... 😊

[@arunselvabio]

Beautiful

[@jaythakur2137]

Good one

[@dijoxavier]

Excellent thanks

[@joez8908]

Good job man

[@thedrei24]

nailed it, thanks

[@emranthabet6592]

One of the videos worth watching regarding the topic. Thank you.

[@dariuszkajtoch6309]

Pretty nice!