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thank you.

classics 0:07 0:08 0:08

this kind of videos remind me of what internet is all about: sharing knowledge. Thanks for the content. I hope internet stopped here

Thank you. Perfectly simplified in 2 minutes. Now I can build on this basic understanding

At 17:34 you said, if g is positive and then log of negative quantity would be infinity. Is it correct? log of a negative quantity is not defined right.

That was amazing, thanks 😊

The least squares error example is beautiful!!!

This is such a great video on so many levels. May god bless the people who had a hand in making it 🙏🏻🙏🏻

Amazing explanation Thank you so much ☺️

hmm close but no cigar

Давно слежу за твоим каналом) рад что у тебя все збс и ты прогрессируешь)

We center the data to have a mean of 0, which allows us to match the form of the covariance matrix provided in the video

Nice video

Man, I just checked that you haven't uploaded any new videos since 2 years! Hope you're doing well and come back with these amazing videos <3

Да вроде кайфовый сайтик. Играл немного, но мне вполне понравилось)

I watched you video, you video is so informative and humble �� thank you for share your video, I follow you

Is the answer to follow up question#1 = 4 ?

Amazing video. Thanks!

@VisuallyExplained Is the answer 2nd follow-up question (the median value) 2 throws? For example, take 100 throws, out of these, 16.6 throws would yield 6 in first throw. Around 41.6 throws would yield 6 in their second attempts. And since we want the 50th throw (or rather avg of 50th and 51st), it would be 2.

I love it! Thanks for that. Can you share the code used for PCA in this video, please? I am trying repeat, but my results dont check with yours, I want to see where I'm going wrong (I didn't find it in the description on github). Thanks for the video.

I really liked the video and the visuals, but I think it would be better without the "generic music" in the background.

at 4:57, "the happiest country seems to be the most balanced ones", seems wrong, it should be "the most power ones" ?

very nice animations, and well explained. But just to be a bit technical isn't what you described called "mini-batch gradient descent". Because for stochastic gradient descent don't we just use one training example per iteration?? 😅😅

You saved me for my Data mining exam tomorrow 🙏

rubbish video

Quick question: How do we choose the gamma parameter in the RBF kernel at 3:00? By, say, cross validation?

Basically NN is f*@k around untill you find a best possibile value

It's like being less restrictive keeps you from optimizing the wrong thing and getting stuck in the wrong valley (or hill for evolution). Feels a lot like how i kept trying to optimize being a nice guy bc there was some positive responses and without some chaos i never would have seen another valley of being a bad boy which has much less cost and better results

Just learn how to speak slwoly and you will have more views

Honestly didn't really help with my questions, but I didn't expect a 3 minute video to answer them. This was very well done, the visualization was great, and everything it touched on (while brief) was concise and accurate. Subbed. <3

Great video!

Amazing video! I could save a lot of time! Thank you very much.

sagapo<3

Brilliantly Genius!

how can v_k be used before it is calculated in the next line? How can one know the 'condition' if this is actual data and not a mathematical formula?

Perfect thank you

Perfect explanation thank you 🙏🏼

great explanation!

That's great!

3:39 It's possible to get less than 1/2 Max-Cut. If all nodes are the same color, that's 0 cuts. We would have to shuffle the list of nodes and split them equally for assignment. Independent assignments will get you something like coin-flip results without a 1/2 lower bound.

Isn’t the one who “goes first” the one on the inside?

Thank you for this clear explanation.

good but why the loud ass shopping music

Amazing video thank you

Awesome video! This is very very helpful (as I'm going to take the convex optimization class exam tomorrow...) However, I am a bit confused at around 6:30. You mentioned that the minimizer x goes first and the maximizer u goes second in the expression at 6:45. I think in mathematics, the expression is evaluated inside-first? So in this case the inner part, maximizer u, would be the first player, and the minimizer x would be the second. I'm not sure if I understand this correctly...

Awesome content and video edition, thank you so much. Do you have any advice to produce such kind of graphics and animation ?

Wow what an amazing video, i understood svm in 2 minutes, which I didn't watching other 15 minutes tutorial

I've finally known where the hell Lagrangian comes from Such a great video

sir do you use "MANIM" libray of python to create these beautiful animations in your great videos ?

loved it