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Hernandez Betty Lewis Kenneth Gonzalez Christopher

Haley Corner

You're awesome.

Moore Kevin Moore Sharon Lewis Richard

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

선생님 감사합니다. 이 영상을 보고 MCMC를 깨우쳤습니다. 오래오래 건강하십시오.

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.

Damn, einops is nice.

I just found your channel today and I am glad I did! Great stuff

Comparing to other videos, this one's fantastic.

Still not very clear to me why that hasting generalization is required? It would have been better if you had explicitly pointed out the problem statement with metropolis that requires generalization solution.

What is t1,t2..tn.. are these different instance of target variable? If so why each has different distribution. Are you assuming these categorical target variable and each has it own distribution? This part is confusing me in all the videos.

P(w|x) - what is x mean her exactly. Probability of weight given different data within training or different training set or something else?

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