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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

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.

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Hernandez Michael Taylor Donald Walker Richard

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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)

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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!

Can you please guide me whether weight vector is column vector or row vector. It is creating confusion in multiplication. Thanks in advance for the great series.

Wilson Jose Lewis Matthew Smith Matthew

Thank you so much.

Thompson Cynthia Martin Frank Brown Jason

Awesome video. Is there any intuition on why we are using reverse KL as opposed to forward KL?

How do we evaluate the target function f, if we assume that it is not known and we want to discover it?

Garcia Larry Lewis Charles Hernandez Carol

Have been using Chris Bishop's new DL book and he reuses the same figure from PRML. Thanks for your video, the general equations are crystal clear now! ❤

Thank you for your work, you are a very talented and valuable teacher

then why not just use f(x), let c*g be a straight line equal to the maximum of f(x)

Amazing!!

Walker William Moore Patricia Perez Anthony

Very good explanation of Kalman filter, thanks for your time and work for that video.