The Cosine Similarity is a useful metric for determining, among other things, how similar or different two text phrases are. I’ll be honest, the first time I saw the equation for The Cosine Similarity, I was scared. However, it turns out to be really quite simple, and this StatQuest walks you through it, one-step-at-a-time. BAM!!!
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0:00 Awesome song and introduction
1:46 Visualizing the Cosine Similarity for two phrases
6:19 The equation for the Cosine Similarity
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Your videos are such a lifesaver! Could you do one on the difference between PCA and ICA?
Please can we have vidéos about transformers ? 🙏🙏🙏
I'm a native spanish speaker, and it surprised me when it started speaking spanish, it will reach more people, but they will miss your motivating silly songs xD
It all seems so easy when you speak about such complicated things! Huge talent! And so funny ⚡⚡⚡
You literally make it so easy!!
I can't help but smile 😊😊😊❤️❤️❤️
By far one of my favorite YouTube channels!
Always thank you for the great and easy-understanding video!
And I have a question about the totally different word.
If there are 2 sentences like very good/super nice, since very, good, super, nice are totally different, the cosine similarity will be 1.
However, they are actually the same meaning!
I want to ask what else preprocessing should we do toward such situation?
Thank you so much!
superb ! Thank you for the explanation
"in contrast, this last sentence is from someone who does not like troll 2" – I was expecting a BOOOO after that lol
I still don't understand how that works for embeddings though. Each embedding dimension should represent loosely a grammatical property of the words, than how can one word that is farther than another in a single dimension (as in your Hello Hello Hello example) be considered identical?
Perfect! I'm trying to figure out how to best present my Single Cell Data in a UMAP and saw i cosine is the default distance metric in Seurat!
Super interesting ! Do you have examples of how those are implemented in practice ?
Cool! (in StatQuest voice)
Can you please tell about some applications of cosine similarity like where is it used in which type of problems?
Excellent and clear video! I wonder why NLP applications use more often cosine distance rather than other metrics, such as euclidean distance. Is there a clear reason for that? Thanks in advance
This is another great video, Josh!
question: @3:51 you talk about having 3 Hellos and that still results in a 45 degree angle with Hello World.
However, comparing Hello to Hello World seems to be a diff angle from comparing Hello to Hello World World.
Is there an intuition as to why this is the case? That is adding as many Hellos to Hello keeps the angle the same, but adding more Worlds to Hello World seems to change the Cosine Similarity.
you are amazing
Awesome video! I had no idea what Cosine Similarity was, but you explained super clearly
nice easy explanation
Could you cover discrete cosine/fourier transforms pretty please?* I've love to know how to break signals up into their component frequencies.
If you haven't already!