I really found that little coffee bean animation helped me to concentrate on the video ! Thank you for this strategy !
@matt.jordan3 жыл бұрын
Such a great explanation awesome work!!
@AICoffeeBreak3 жыл бұрын
So glad you liked it!
@omarlopezrincon2 жыл бұрын
ha ha ha, i was hoping to learn how to calculate the eigen vector, I love this channel
@quote.d2 жыл бұрын
Thanks! Great explanation and visuals, and I especially enjoyed whispered parts. I'm sure I'm not the only one who gets this emotional reaction to information that is being whispered instead of plainly said. And emotions are very important for remembering things. Please consider making a full-whispered redub of your videos!
@AICoffeeBreak2 жыл бұрын
ASMR with Machine Learning content. 😅
@EGlobalKnowledge2 жыл бұрын
A very good explanation with details of how it works
@vincetechclass3390 Жыл бұрын
Nice presentation. Pls, what tool did you use for presentation?
@AICoffeeBreak Жыл бұрын
Thanks! I used good old Powerpoint. 😅
@chenzakaim32 жыл бұрын
you are really awesome!, thanks a lot
@AICoffeeBreak2 жыл бұрын
🙂 Thanks for watching and leaving this awesome comment!
@kellymarchisio3772 жыл бұрын
First off - loving the videos! Thanks for the fun and clear explanations. Quick clarification, though: The matrix V at 5:22 is drawn as a D' x D, no? Are we meant to actually have z_i = x_i V^T (V-transpose)?
@KevinTurner-aka-keturn2 жыл бұрын
I tried doing some dimensionality reduction using yellowbrick and sklearn on what I _thought_ was a very modestly-sized data set, and I was surprised by how long it took! I guess it was probably the Manifold Learning methods that took longer than PCA, but I don't recall PCA being exactly quick either. Is that expected? Are there techniques for subsampling data to get some faster approximation?
@dontaskme16253 жыл бұрын
Wouldn't a coffee bean be afraid of a coffee break because that's the point in time when it would be most likely ground up?