How to apply t-SNE and interpret its output: Dimensionality reduction Lecture 25@ Applied AI Course

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[@surbhikumari6734]

Love love love thanks a lot... wonderful explanations👍...watched your full playlist on DR.

[@woodworkingaspirations1720]

Back here again. Litsening daily.

[@DM-py7pj]

excellent

[@woodworkingaspirations1720]

Some claim that perplexity is not a very reliable hyper parameter.

[@SindyanCartoonJordanian]

Great, Thank you

[@hamzasaaran3011]

Very good explanation!

[@sanjaykrish8719]

Good one. but see at 1.5x or 2x

[@user-qz7pk1yc7c]

Very nice

[@Yangyang-1995-]

you are so funny ahah
great tutorial!!

[@NS-te8jx]

what software are you using for recording this?

[@yashodhanbhatt]

Correct me if I'm wrong, but as per your (by the way excellent) explanations about the drawbacks of t-SNE, it would be incorrect to use the output of t-SNE as an input to a clustering algorithm, right?
As you explained that t-SNE expands denser clusters & contracts sparse ones, and secondly, the distance between the clusters don't mean anything, so we can not aim to cluster the t-SNE output & check for visible clusters. Am I thinking correct?

[@syedhasan773]

really wanted to know the answer to this

[@woodworkingaspirations1720]

I think you can not predict the class of a new data point because of the stochastic aspect. But the clusters are meaningful. Others can comment too.

[@handing2857]

saves my ass for exams!