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Great video!

I have 2 questions. How are the 1X1 and 3X3 CNN used trained to obtain the weight parameters? Also shouldn't 3X3 with stride 1 change the dimension, though it keeps the number of channels the same the size of the output feature would have changed and reduced by 2

Your videos are amazing, very clear and concise explanations!

This is the best series I have watched on explanation of object detection algorithms. Also, your approach of asking fundamental questions like "Why in this case, a metric of evaluation can't be used as a loss function" and many more such questions you addressed were so insightful. Really grateful for the efforts you've put in here. Thank you. 🙏🙏

At 11:02, instead of "the first rows of the first four channels will be for box coordinates", do you intend to say "the first cells of the first four channels will be for box coordinates"?

Thank you very much for your video. But I have a question: when g(x) is a function U(a,b), the probability of choosing Y which obeys g(x) is the same for all Y in (a,b). But when the bounding function is Gaussian or something else (i.e. when simulating g(x) we cannot guarantee the probability of selecting each point is the same), does it affects the probability of selecting X which obeys f( x) ? ( Because if the simulation of Y is not equivalent then even if the next steps are effective, we cannot guarantee that we give equal chances to all points X when starting before "reject or accept")

Simply beautiful ❤️

Thanks for the lecture sir! I have a question at 4:54, how did you expand that E[log_p_theta(x)] into Integral(q(z|x)log_p_theta(x)dz)? Thanks!

Thank you so much, First I read the 1st chapter of this book and then I listened to your video. You gave a superb explanation and cleared all doubts. Thanks for your community service :)

Amazing explanation!

Thank you! One request -- can you explain the reason behind the equivalence between assuming that the target variable is normally distributed and the assumption that the errors are normally distributed. While I understand that the two assumptions are simply the two sides of the same coin, the mathematical equivalence between them appeared to me like something that is implicitly assumed in moving from part 2 video to part 3 video.

Very articulated explanations, really appreciate it! thanks!

❤❤🙏🏽🙏🏽🙏🏽 thanks and subscribed

Thank you for your hard work, which enables us to understand crystal clear. Thanks a lot ❤❤❤

Hi, I have got micrsoft form recognizer api which gives bounding box of 8 coordinates for a given class, how to draw bounding box using that. for eg: bounding_regions=[BoundingRegion(page_number=1, polygon=[Point(x=33.0, y=496.0), Point(x=169.0, y=496.0), Point(x=168.0, y=532.0), Point(x=33.0, y=532.0)])] they haven't provided in the documentation as well, if you could help, I would appreciate it. I have converted it into list like this [33.0, 496.0, 169.0, 496.0, 168.0, 532.0, 33.0, 532.0] but don't how to plot.

Excellent

Thank you for these very clear and visually efficient explanations. I'll make sure to use these concepts in my PhD work !

I thought temperature was like getting a fewer and saying random things:)

The best! Thank you sir

At 5:01 could you please explain why is it [1,5] and not [5,1]? Shouldn't the coordinates be in (x,y) format?

Thank you a lot for your videos! Selection of subjects in your series is excellent, every tutorial offers very interesting information.

One stupid question here, Why we were interested in finding max joint probability, in the first place?? were there any other way to find w and beta??

It learns the parameter right?

At 6:39, the distribution p_\theta(x|z) cannot have mean mu and stddev sigma as the mean and std dev live in the latent space (the space of z) and x lives in the input space.

Watching your videos keeps reminding me of the phrase “a picture is worth a thousand words”, to which I want to add “ a great picture is worth thousands in gold”. Many times I had to freeze the video to let a particular moment sink in, because I couldn’t believe the insight that picture brings out . ❤❤❤

I am grateful to your lectures ❤ what a wonderful service you’ve done to all the learners

So, in-depth and with those visualization it is a grate learning experience

In real life we don't know Target distribution - f(x). How did you calculated alpha for various sample points ? f(Xt+1)/f(Xt)

We are okay with imperfections as long as they are useful to us ... great wisdom🙏

Amazing. Thanks for posting.

At the 4:45 mark, how did you expand the third term Expectation into its integral form in that way? How is it an "expectation with respect to z" when there is no z but only x?

my feedback is that this is amazing!!! wish my ML prof taught this :( this truly is one of the few videos that breaks the sum rule, product rule, join and marginal probability down so well

Great video; thank you :)

How you plot this? which software you are using?

THANK TYOY SO MUCH

I am worried that he hasn't uploaded videos in 7 months.... is everything alright?

Amazing lectures, thank you so much! I was wondering at 12:07 the partial derivatives of E wrt w_i (in the right panel), shouldn't those be the partial derivatives of ys and not Es?

Thank you, very good overview. You must be having thorough understanding of many object detection models to deliver this kind of overview. I have one question (only for discussion): How it is "clear" (1:22) that object detection is difficult task for machines? I think it is important to mention why the problem is difficult (challenges) to solve from computer vision point of view. You did mention a couple of challenges at 10:40 but these are w.r.to DL approach.

You just saved a college student living in South Korea! Thanks for amazing visualization and explanations!

best MCMC video I have seen

1:31 how do we not know how to sample as the distribution function is already given and you also plotted it ?

thanks

This is pure gold ❤

5:35 Bayes Rule ... intractable computation.

Clear and easy to understand compared to other videos throwing lots of math formula at the begiinning. Great work! subscribed your channel

Thank you so much sir for the amazing explanation and visualization.

If done with UNet, it won't require upsampling as we concatenate the layers right?

Just Mind-blowing loved the way you explained concepts and slowly built on it, that's how inventions and human mind works and mathematics is just a tool to realize/record complex ideas. Other lectures directly jump into maths without explaining the idea.

Still you didn’t explain the need of CPDF, also you told that we want to get rid of evaluating integral therefore we want something…? What CPDF , but CPDF is integral of pdf

Spectacular 👏