Відео

IIT JEE 1984 top ten ranker. Gold medalist from IIT Kanpur, batch of 1988.

I think Laplace smoothing applied only during predictions. Why he is applying for parameters also??

28:00 A unique view at attention. In this image all 6 nodes are related with all 6 nodes in self-attention case. And in cross attention it would be like set A sends a message to nodes in set B. And voila, it's a fully-connected layer! But with tokens passed instead of values

Andrew ng also took same kind of example to explain LM.

I think at 37:10 professor did not make it quite clear for probability = 0. The student confused probability with possibility. It is totally ok for thing A that is p(A) = 0 to happen to some extent. Am I right?

Please put the subject of the talk in the title. You can then market the OpenAI speakers

Hi. Can anyone recommend any textbook that can help in further study of this course. Thank you

How do we know what is small vs large? For example, with emergent tasks, it highlights that more data could lead to more accuracy with enough compute. The small LM would have not seen accuracy improvements but the large LM did. For the tasks currently indicated as flat, couldn't we just not have enough compute now to know if these tasks would get more accurate?

I could be wrong... But as I understand what mister Lamport is saying... This is just digital design... Combinational... Sequential circuits... I could also be wrong but... Clocks are more of combinational circuits... On the other hand, sequential circuits have clock circuits in them... 🤷

The students were asking some great questions, no wonder I don't go to Stanford

Surprised by the amount of hair an AI scholar may have retained.

100x😊

Thank youuuu

7217 1:07

Great introduction on deep generative models!

In the poker question, probability of A' is 42 options, right ? Since one of the 7 cards already on the table is A of clubs ?

Thanks for sharing this

This video is crazy 🔥🔥 interesting to know how crazy defi has grown and it’s great to be able to see this seminar through UA-cam it was an interesting chat to listen to defi is growing globally it’s not just in the us now it’s in other countries take in mind I am commenting from outside of the U.S crazy stuff excellent content 👍🏻

Shouldn’t there be a different UA-cam channel for AI from Stanford.

Strange world. This dude is almost a kid and gives a lecture

Love the section on "kale divergence"! Thanks UA-cam auto-captioning! 😂

Great lecture. But some time a little faster pace than Christopher Manning.

I am addicted to Prof. Jure's accent now😂!!!

Very clear and interesting lecture

omg his forehead

it's a good idea, I like this subject

1:03:51 that website what the Andrew doing there

Great points. Maybe better for those starting out. Once you’ve been in a career with family, life has a way of creating boundaries. I’ve attempted many times to break out and do something different only to find myself unable to change. Interviewing for a career with experience in a different area yet same industry, likely you will be judged on your experience rather than your ability and willingness for change.

Azerbaijan❤

Thank you!

Too Good to be Honest.

Where can I find the problem sets? This is really import to me. Please someone help me!

Thanks for sharing.

wow

thanks

❤❤❤❤❤❤

I love 💓💓💓💓Stanford Online💓💓💓💓

I firmly disagree. There must be an important distinction here: (a) expectation of growth by simple treasuring of commodities and currency; (b) expectation of growth by active exploration of the anticipated value by a third party in order to produce more value. The test must cover only option b, according to the reasons stablished in the video.

The dice example for independence is wild! If event G sums to 7, it's independent from E or F but if it sums to a number less than 7 it's not? Would someone be able to explain this in some detail? Or provide some intuition? Thanks!

Where to find the "other" videos that Andrew says the students can watch at home?

This lecture is super useful. really appreciate.

Just awesome 😊

Love to maddie <3

where is the full playlist?

is this course still applicable in 2024 guys. after a lot advancements will this be sufficient to get started?

learning probability for 12th grade from standford lol

two great minds of nowadays.

Great presenter.

00:10 Today's discussion is about supervised learning and locally weighted regression. 07:48 Locally weighted regression focuses on fitting a straight line to the training examples close to the prediction value. 16:15 Locally weighted linear regression is a good algorithm for low-dimensional datasets 22:30 Assumptions for housing price prediction 29:45 Linear regression falls out naturally from the assumptions made. 36:36 Maximum Likelihood Estimation is equivalent to the least squares algorithm 44:40 Linear regression is not a good algorithm for classification. 51:04 Logistic regression involves calculating the chance of a tumor being malignant or benign 58:30 Logistic regression uses gradient ascent to maximize the log-likelihood. 1:05:36 Newton's method is a faster algorithm than gradient ascent for optimizing the value of theta. 1:12:40 Newton's method is a fast algorithm that converges rapidly near the minimum. Crafted by Merlin AI.