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How is this different from U-net? I think they're pretty similar if you think that in the U-net you're going down in the encoder, up in the decoder and sideways with the skip connections. It's like an upside-down U-net

Professor Kaiming He is the GOD of Deep Residual Networks .

How can we use these (predictions_box, predictions_obj, predictions_cls) information from the decoder and create visualizations on input image ?

Thank a lot, this was an amazing explanation! just a question. Why do we need anchor boxes to be a pre-set value when we can just use the original bbox values? Like we use the center of the original bbox and its W and H values. I dont understand this

This ground-up approach is excellent 🙂Thank you for explaining...

Thank you for posting this great video! At 3:53, why did you use a "squared" Euclidean distance, instead of an Euclidean distance? I wonder if you use an Euclidean distance, the properties of the "squared" Euclidean distance are the same?

Amazing !

Very intuitive explanation. Thank you.

Excellent overview. Highly appreciated.

Amazing, thx a lot.

What a wonderful explanation

Thank you so much! Your video really helped me understand the method and remember it, which is equally important. 🙂

It was fantastic. The most informative video of KL divergence

I rarely comment on these kinds of videos but this really was a great exposition, well done.

That's a great explanation. Thanks a lot!

Can you turn on the transcript for this! Great explanation!

He is good! Very good to say, for example, we want the average difference, but when talking about rv we talk about expected value ... . And many other very careful explanations.

Thanks for sharing the knowledge

Kudos for explaining very well. I wish to comment on just one thing. @12:08 I could not see the two colors (red and blue) clearly in the video. Maybe the picture can be made to have white background to make the colors more obvious.

Your videos are very helpful, we want more on object detection! Please sir 🙌 🙏

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.