This is really good. Concise , straight to the point, and there is no need to show a line of code !
@rt585283 жыл бұрын
Being a math lover, within a minute of your explanation I became your fan, was always in a search of videos like this
@mango-strawberry5 ай бұрын
true. his channel hasn't been picked up by KZbin yet.
@stanlukash332 жыл бұрын
I started googling tf-idf and then I was like "Hey, maybe that guy has a video on it", and you do! Thanks!
@ritvikmath2 жыл бұрын
😂 "that guy" says you're welcome
@stanlukash332 жыл бұрын
@@ritvikmath haha sorry, Ritvik!
@robertc63433 жыл бұрын
Excellent teaching! Perfectly designed, clearly explained and not even one sentence that would be redundant. I’m your fan my friend 👍🏼🙏🏼
@ritvikmath3 жыл бұрын
Wow, thank you!
@leod17402 жыл бұрын
@@ritvikmath Yes excellent explanation
@hemantsah85673 жыл бұрын
Your videos before sleep... Keep nightmares away...
@ritvikmath3 жыл бұрын
aww, thanks !
@imdadood57053 жыл бұрын
I wish to have your coherence when explaining. Awesome explanation as always.
@emna143 Жыл бұрын
Hi, first of all, thanks for the great explanation. I have watched your videos about Word2Vec and TF-IDF, and I need help, please. I'm a student working on a project about binary classification of SQL injection attacks. The dataset I have contains two columns: 'sentence' and 'label.' I need to extract features, but I'm confused about which technique to use: Word2Vec or TF-IDF. Can you help me decide?
@_instanze_ Жыл бұрын
Youre an excellent explanar man. And I don't mean that lightly (I rarely compliment people wallah). You got a knack. Truly! Subscribed!!
@ritvikmath Жыл бұрын
I appreciate that!
@hannahb.94544 ай бұрын
This came in clutch, thanks
@michaelhaag33672 жыл бұрын
Lucid explanation, my man back at it again!
@bananalord85753 ай бұрын
Sweet and simple!
@ralphhennen57693 жыл бұрын
How do you model multiple objects associated to a term class: Dental Care: United Health Care, Blue Shield, ..., by state? This becomes contextual and local within the text - how close is the word dental care in the text to UHC, for instance. The result would show which states address dental care in their health insurance regulations and which insurance companies make it available - both in a positive and negative way. Understand that this is a narrow example. Thanks
@balaganesh3440 Жыл бұрын
Outstanding explanation!
@hameddadgour2 жыл бұрын
Great presentation!
@ericzhang59872 жыл бұрын
Excellent explanation !
@devidurga392 Жыл бұрын
clear cut explanation. Thank you
@mupetman12142 жыл бұрын
Would you advise to take out stopping words and run tdidf on the new set of documents?
@robertodigiacomo39102 жыл бұрын
Great explanation
@MrKqsami Жыл бұрын
Great Job sir!
@gunbac743 жыл бұрын
I read this explanation in a book, but not as clear as this video. Well done!
@ritvikmath3 жыл бұрын
thanks!
@fustigate89332 жыл бұрын
Nice explanation!
@mariapazherrera43062 жыл бұрын
Amazing!!!!!
@athena9357 Жыл бұрын
You saved me! My professor explained this in 3 hours, I watched it 2 times and I don't get it. This guy explained the same concept in 7 minutes and I get it!
@srijitbhattacharya6770 Жыл бұрын
Excellent , simply briliant
@mango-strawberry5 ай бұрын
damn.. that was a solid explanation
@MYanton19942 жыл бұрын
thank you very much
@almonddonut18182 жыл бұрын
Thank you so much!!! 🤩
@sia-watsonlee Жыл бұрын
amazing
@mitadrubanerjeechowdhury90923 жыл бұрын
Amazing stuff, thanks man for letting me pass the exam.
@0xjrr3 жыл бұрын
love that in this alternative timeline the last speech is from Obama
@skeletonrowdie17683 жыл бұрын
a certain president would really bias the vocabulary data
@vasundharasingh82166 ай бұрын
in cases where all the 3 documents contain the word, even if 2 of them contain the word only once and the 3rd doc contains it a 100 times, tf idf would be 0 as idf would be 0. isn't this misleading then?
@RedditFam2 жыл бұрын
Very useful! Thank you Sir!
@ai-force3792 Жыл бұрын
very Good
@ritvikmath Жыл бұрын
Thanks
@sepideh1111 Жыл бұрын
Thanks , great teacher if I could I would have given you 3 thumb
@durasaksham6 күн бұрын
It was that easy
@aleynapolat1545 Жыл бұрын
Bro, you are a good narrator but a bad organizer. It would be better that the next time you write on the board more regularly in order to make it easier to follow what you sayin
@australianperson25823 жыл бұрын
Thanks for politicising education with that exclusion with that example, unsubbed - so partisan.
@ritvikmath3 жыл бұрын
Sorry to see you go, it was not my intention to politicize but rather just to use this as an example.
@Scar_ Жыл бұрын
Thank you for this! You saved me much time! Your explanation is legit!
@ritvikmath Жыл бұрын
Thanks!!
@VishalKhopkar1296 Жыл бұрын
if the word 'healthcare' did occur in all 3 speeches, but occurs in the Obama speech 26 times, but only once in Clinton's and Bush's speeches. Using this mechanism, the IDF of healthcare would still be 0, but since the word has been used a considerably large number of times in the Obama speech, it is definitely important
@redpz11 ай бұрын
in a more realistic situation the # of D would be much larger so cases like this would be extremely rare
@TheTranscending10 ай бұрын
Good point
@martand_053 жыл бұрын
What a classy explanation. So good man!
@ritvikmath3 жыл бұрын
Much appreciated!
@MultiRockxD Жыл бұрын
Is this a good tool to create a top of "important" words in a dataset? or it just helps to see the relevance in a particular document, I want to use it so I can maybe sum all the tdidf of all the documents and create a top words but I don't know if this is the best approach/solution to what I want, thank you in advance
@Shaan11s7 ай бұрын
YES! I get it now, much love bro
@vinson22333 жыл бұрын
I'm really glad to choose this video instead wasting my time watching 30minutes explanation of tf-idf. Great job for explaining this
@ernestanonde32182 жыл бұрын
Powerful...Thank yoiu
@swagatggautam6630 Жыл бұрын
I wonder why my teachers couldn't explain so simply.
@k_anu73 жыл бұрын
If anyone dislikes this explanation god will have to come down to explain him/her.
@vaibhavmourya65 Жыл бұрын
Great explanation buddy🙌🏻
@butterfly344575 ай бұрын
So simple and concise! Thank you so much!
@pushkarparanjpe3 жыл бұрын
This is a great explanation. Thanks. I have a question about differences between the implementation described in this video and another implementation commonly found on the web. Can you explain how these two details would impact the final representation: 1) Term frequency simply calculated as term count 2) Applying vector normalisation (L2) to the document vector obtained in this video Another question which is more open-ended: why is TfIdf still relevant ? Or less provocatively - is there a sweet spot where one would prefer TfIdf over the modern dense vector representations (such as word2vec, doc2vec, etc.) ?
@nehimomo2 жыл бұрын
veyr great explanation, much better than my lecturer
@pallavijog912 Жыл бұрын
Nice explanation. Thanks!
@Justrelaxx101 Жыл бұрын
Perfectly explained
@adrianramirez97292 жыл бұрын
Amazing explanation!
@amadios98742 жыл бұрын
That was crystal clear, thanks
@chaitu20372 жыл бұрын
Very well explained
@user-or7ji5hv8y3 жыл бұрын
Clear and concise.
@warislthong31498 ай бұрын
Excellent !
@Begooder3 жыл бұрын
many thanks
@BhuvaneshSrivastava3 жыл бұрын
I like your videos first and then start watching your Data Science videos because I am sure that after I am done watching it, I will like it anyway. Keep it up.. 🙏
@ritvikmath3 жыл бұрын
Wow, thank you!
@22malman3 жыл бұрын
Superb!!
@summerxia7474 Жыл бұрын
Such a clear explanation!!! Much better than my teacher in the class. Why can't they just make it this simple? Thank you so much.
@ritvikmath Жыл бұрын
no problem!
@maefiosii2 жыл бұрын
that explanation was so smooth and clear.. great job
@ritvikmath2 жыл бұрын
Thank you :)
@elsywehbe28977 ай бұрын
Your examples are excellent! Thank you!
@ritvikmath7 ай бұрын
You're very welcome!
@AKapich Жыл бұрын
Very succinct explanation, thank you very much
@ritvikmath Жыл бұрын
You are welcome!
@eramy13 жыл бұрын
Good explanation in a simple way... keep doing well man
@ritvikmath3 жыл бұрын
Thanks a ton!
@sebastiancamilopuertogalin44782 жыл бұрын
For any given word/term, we want to know how important is that term for a given document, relative to the entire corpus of documents. E.g. for Clinton these subset of words is really important in his inauguration speech, relative to the other inaguration speeches. TF-IDF is simply a multiplication of the metrics TF (term frequency) and IDF (inverse document frequency).
@superbatman14623 жыл бұрын
Nice Explanation
@ritvikmath3 жыл бұрын
Thanks!
@negusuworku18717 ай бұрын
iT IS REALLY NICE. KEEP IT UP
@ritvikmath7 ай бұрын
Thanks a lot 😊
@cleansquirrel20843 жыл бұрын
Awesome video!!
@ritvikmath3 жыл бұрын
Thanks!
@mila4real111 ай бұрын
Cool! Loved your simple but extremely efficient explanation
@aradsoutehkeshan4740 Жыл бұрын
Useful :)
@ritvikmath Жыл бұрын
Glad you think so!
@luuz_study_yt71232 жыл бұрын
Thank you for the video, we are working at a Movie recommender System and this helps a lot for NLP.
@alexfeng757 ай бұрын
great video with depth and simplicity at the same time!
@cesarreinoso22032 жыл бұрын
Awesomeeee Simple and Clear
@dalvirsingh40703 жыл бұрын
Explanation was awesome!
@David-nw6rz3 жыл бұрын
When using the whiteboard, your videos are even better than with pen and paper! Thanks for your videos!
@xxxxxx-wq2rd3 жыл бұрын
but if healthcare appears 100 times in one document, and only once in each of the other 2 documents, then the result will be zero!
@nicholasdavis95293 жыл бұрын
This was my question. If you found out let me know.
@nicholasdavis95293 жыл бұрын
Great video btw, best explanation.
@zephyrsurfteam3 жыл бұрын
Great video! Thanks! I would love to see more content on TFIDF.
@ritvikmath3 жыл бұрын
Noted!
@abdulbasit01234 ай бұрын
That was a great explanation, Thanks 🤍
@jb_kc__6 ай бұрын
your explanations are great bro cut to the heart of the issue + ensure conceptual understanding 🫡🫡