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sir you deserves the most slopiest bj from my side gawk gawk thank you very much

Thank you for the clear and concise explanation.

The best explanation ever. Thank you professor

Great video

So perfect

Thank you for this great video. Why don't you use Kakutani's FPT? So we don't have to worry about the adv fnc being a one-to-one mapping?

geniusssssssssssssssss , my heart is so full after watching this video

I can't remember the last time I commented on a youtube video as it was much too long ago, but I just had to because your videos on deeplearning are CRIMINALLY underrated. I have yet to find another resource that explains ResNet so intuitively as you break down each concepts to laymen terms and you take your time explaining them. You have an amazing way of explaining concepts and I sincerely hope your videos get all the recognition they deserve!

Good day, am having difficulties understanding how to point out the independent set and vertex cover from a graph

@zachrayman7879 is correct that the cell (a,cf) should be (2,2). Reply is also correct in that in the subgame after player 1 plays b player 2 moves e so (a,cf) is not subgame perfect

I have been stuck at 6:56, why are the slack finish times: 1, 11, 15 and 17? Could you please explain the calculations behind that

Great explanation, it helped me a lot. Thank you for taking the time to make this video!

Wow amazing tutorial. Thanks ❤

Thank you very much, This is great. I'm watching this again and still super helpful. So delta is actually the upstream gradient, right? I only think that it would be nice to mention what is a computation graph, but otherwise, super helpful.

Thanks, you are the best ❤

great tutorial!

Bro is underrated asf

very nice 🎉

thank you so so much for this video!

Wow, so clear! That was stellar, thank you!

A true legend

shouldn't folding have a utility or expected value of zero. since no money goes into the pot how can it return -1 in value?

thanks. Sorry but the video you mentioned in the last seconds of this video in not yet uploaded(video about stability proof)

That was amazing! So clear and concise explanation. Thanks!

Thank you for your good explanation, helped me a lot on my deep understanding journey of all these mechanims 😊

AMAZING!!

In this example iI think Sj >= Fgi because if Sj is smaller then there is a conflict in task and the task is not compatible instead the Si should be after the finish time of last request

speaking about the error value and calling it a Loss value using that term out of original context, makes this confusing to the new learner...

thank you so much Prof

Thankyou. That was quite a puzzle to me and now I see the instructions. Thankyou. Appreciated much

understood

great

where i can find his full course?

Thank you

great teachings proff! keep up the great work

Such a great explanation. Love this!

Your explanation is great.

I am still confused. What are the prerequisites to understanding this video.

That was amazing. I am surprised by how I understood the whole thing and didn’t get bored or confused through out. Well done

tfw you click on a video and it's your old college professor lmao

Professor Bryce, you sir are a god. Thank you for this series.

Free books

Thx dude u are awesome !

This is godsent, thank you

Thanks for nice explanation But I have one query, in part 16:00 where you said "each output neuron get input from every neuron across the depth of previous layer", here doesn't that make each output depth neuron same??

Thank you Professor! This introduction is really helpful and detailed!

you saved my life

Professor Bryce, you are a splendid teacher. I enjoy watching all your course's videos, especially the explanation depth - and at the same time pinpointed simplicity - are amazing. Thank you! Have a nice day!

Isn't this similar to RNNs where subsets of data is used for each epoch & in residual network, a block of layers is injected with fresh signal, much like boosting.