Best lecture on Kalman Filters. Thanks.

Absolutely excellent. Thank you very much!

What an amazing video! Congratulations on your work!

I don't know if the sum of two independent normal variables has 2 peaks..

great place to learn Are not you making other videos on latest yolo models bringing new methods

@kapil How did you think of formulating the problem as p(w|x, t)?

This channel should already have a million likes and 10 million subscribers!! 🎉🌈💛💡

Excellent! That was an amazing tutorial!

Ah, feels interesting Got to know about Contrastive Learning through a paper that we had to read as part of our coursework, which was on Probabilistic Contrastive Learning (nicknamed as proco) Will explore this more in the coming months, thanks for the simple explanation ! You sound like a young excited prof, takes time to explain obscure stuff

18:18 shouldn't it be inverse of f of x instead of f of x

I am only learning bayesian filters and I am yet to get any other material that explains it like you did in this video. A huge part of the work I recently embarked on! Thank you

Thx so much!

Professor any good tutorials or books can u suggest to start from estimation theory to a bit advanced stats we can learn and moreover how much statistics we need in ML feilds can u tell ?

Awesome explanation!

Sir, thank you for your explanation. However, may I ask to confirm whether I grasp the information in a right way. So the kernel counts are based on the input channels and the filter bank counts are based on the output channels? If thats the case, in the case of 3 input channels with 2 output channels, does the first feature map will be use as addition elements to the last 2 feature maps or through averaging? Or in the case of 4 input channels and 2 output channels does the first feature maps will be added to the second one while the third feature map will be added to the fourth one to make it 2 feature maps? Thank you for your attention

Excellent

Thanks for this informative video! Here is a link to the detailed balance paper as the ones in the description are not accessible any more: kkhauser.web.illinois.edu/teaching/notes/MetropolisExplanation.pdf

What is the meaning of realization of a Random Variable?

fantastic video. thank you

In 3:13 why do you refer to x=0.6 as the first value while immediately after you refer to x=0.6 as the first sample? shouldn't x=0.6 be a single value from a sample of data?

so clear and good

Very good video! I just have a question on 12:07 : Once we know that the derivatives are x^i, what allows us to just write x^i there in the formula? i is only the exponent of xn and I can’t see any sum iterating over „i“ or anything clarifying which i I need to use? I find it quite difficult to understand that part of the formula during those steps. Only after transforming everything into vectors and Matrices „i“ disappears and the formula becomes readable again.

Amazing

Kapil Sachdeva ji . Thanks very much for clearing my doubt over Bayesian equation inherently using marginal distribution. You really are a great teacher 🎉❤

the CV model has detected a good boi

Franco Neck

Wonderful video, thank you so much. Your style is very pleasant.

perfect Thank you

Sir I am asst prof Bioinformatics I am using this for molecular phylogenetics...thank you so much sir for this video

Thanks for the extraordinary job done! Helped me get a real quick grasp of the meaning of the paper.

Clement Landing

How can we just sample from the target distribution in order to calculate the acceptance ratio? Doesn't that defeat the purpose of the algorithm: Why wouldn't I just take samples from the target directly? Or is the problem that we are not able to draw independently from the target?

Hernandez Betty Lewis Kenneth Gonzalez Christopher

Haley Corner

You're awesome.

5:57 gave me the aha!-moment. Thank you so much!

So the bounding box comes with labeled data ?.....or we ourself are creating bounding box

What a clear explanation ! A gem.

Exactly what I was looking for, thank you!

Brilliant explanation! Thank you so much!

Thank you, the tutorial helped me a lot to get started with Einops.

209 Lisandro Ridge

Hernandez Michael Taylor Donald Walker Richard

8831 Osvaldo Heights

Excellent explanation. Please, continue doing that.

when we were calculating Pr(x>5) what is the role of h(x) here ? Cant we just use p(x)

Pagac Road

I have a question if it is possible for the sum of probabilities for future state to be greater than 1 as in the case of s3 at 14:04 in video...? It seems it should sum to 1 always.

Incredible explanatory skills!

awesome explanation!