Video

excellent explanation, as a data science from a biology background this really helped

Your class is good 🙏🏼

You’re my guardian angel

Does this enable older designers to show the AI an image of their hand-drawn designs and get a STEP or IGES encoded 3D file for use in applications? I am looking for a use case where mixed reality headset cameras can capture image information and process it into a shareable 3D format!

4:37 Say if the p(X=4)=0.5 What is the interpretation of this exact statement? Could it be that the probability of x occurring arbitrarily close to 4 is 50%?

Thank you

You’re my guardian angel

AHH!!! I’ve been trying to find more content from you since you left Talking Machines for years!! So glad I finally found this! I wonder how to fix the squeaky pen 🤔

Very nice intuition video with the perfect amount of math!

Really well explained

I'm wondering about the importance sampling. If I understand correctly, we need both pi(x) and q(x) pdfs to use IM. But shown in the previous video "COS 302: Pseudo-Random Numbers", we can draw samples for any arbitrary pi(x) using the CDF + uniform rand trick. In that case, why wouldn't we use the trick with pi and draw from pi directly? I know there are cases where IM is useful, especially in ML/RL for learning or estimating some expectation from _offline data_ . But I can't see the reason why we choose to _sample_ from q instead of pi when we have both pdfs. What am I missing?

Underrated channel

Great explanation !

Very beautiful. Thank you.

Just brilliant.

Awesome. Thank you.

Excellent explanation!! Thanks

How does this whiteboard works?

Thanks

Is the ground truth set weighted by a average, max, common, rare, gravity, edges, node distribution or what?

Thank you for the very clear explanation! I never took a stats class, so online resources like this help me survive upper division CS and ME classes. Much needed for fluids labs and speech processing!

Thanks so much, I like how you explain

Thanks so much, nicely explained!

PLEASE use another pen, I can't finish the video. Great explanation anyways!

Thanks a ton!! It wasn’t only intuitive, you explained what is in the book with the exact notations which makes it easier for me to go back and solve problems there.

Amazing lectures!! I assumed that i will flunk my class before I watched these. You somehow make it sound simple. Thanks a lot

I frickin love you man.

These videos are so amazingly awesome!!!

loved it thanks !

Thankyou so much 💯

astonishing video Intelligent Systems Lab. I shattered that thumbs up on your video. Keep up the very good work.

Succinct.

As a data engineer from a non-CS background, it's one of the most helpful materials I found on the internet for linear algebra. It gives a great intuition to understand the math and real-world examples. Huge thanks!

I like your explanation, but the sound of your pen hitting the board is extremely disturbing!

Amazing vid!

True that! Some Jupyter examples would ve made it complete :)

Thanks 👌👌👌👌👌👌👌

this is awesome I just need to find the time to watch all these and take notes

Best explanation of this material I've ever seen. Thank you!

The explanation is simple and elegant, thank you so much for making this brilliant video! I finally understand Bayes Theorem and marginal distribution!

So well done! And the graphic design of 3B1B helps a lot to maintain continuity with Grant's content so that even new viewers aren't disoriented by different visuals.

very good video

Please help me, how can I synchronise the animations with the audio? What I am thinking is that I should give long pauses by using `self.wait()` and then trim the video according to the narration. I suppose this is not the best method, please share your method if you have better one.

This is a great topic.

This is wonderful. Thanks!

You're a gem, exactly what I needed !⚡

good explaination