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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 👏

Thanks a lot for this clarifying video.

this explanation was amazing! thanks! sub!

I don't know if I got this wrong but if I take a 1x64x26x26 feature through a convolution that has a K=3 and S=1, I will definitely not end up with a 1x64x26x26, but with a 1x64x24x24. To achieve the desired shape would require a P=1. If I'm not correct, would someone please explain how the dimensions would work in this case?

I didn't get a headache!

thank you for your detail and understandable explanation. The video animations are great and impressive.

Kapil, I will be forever greatful to you for this lecture series.

Great Video !! Thanks.

Great content. Will you be able to give a quick walkthrough of any one of the code base like yolov7,8,9 etc. ?

Greate explanation

Thankyou so much

Well explained.

Thank you for the content. I am a bit confused about the motivation, is it sampling? like being able to sample from a distribution? or estimating probability distribution? Because, for sampling, we would first need a distribution. On the other hand for density estimation, we would need to sample (like in VAE). So I am confused about what comes first? Or are these the same problems described in different ways? Thanks.

this is the most clear explanation among all i have read/watched! thank you very much

Really well explained. Thanks for this tutorial!