Perfect explanation!!

Great lectures. I have a question though. Why on 21:34, in the multi-variable chain rule calculation of partial derivatives (of loss) by h we use partial derivatives (of loss) by y, and do not use partial derivatives (of loss) by V, which we calculated on the previous step? I'm sure it's a silly question :)

You should do more. Your videos are far clearer than anything else on the market.

This was perfect, thank you!

Nice presentation.

Very good!

Very good lecture!

Pure gold! Thank you.

thank you for actually explaining the filter operations and the effect on the output dimensions :)

Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012

LeCun, Y.; Boser, B.; Denker, J. S.; Henderson, D.; Howard, R. E., Hubbard, W.; Jackel, L. D. (1989). "Backpropagation Applied to Handwriten Zip Code Recognition"

Perfect explanation, but don't we have softmax operation in the practical SA just like simple SA? I could not see softmax in the representations of practical SA (18:42) unlike simple SA (05:16).

Thanks for the information

Thanks for the course

Thank you for this video!

Very useful lecture. Thanks for sharing! It may be a small typo (at 18:25), should be l = loss(output,target) # "output" instead of "input"

I didn't understand why adding gaussian noise to image does affect the accuracy of discriminative model. (I think it doesn't) In reality models are robust to noise

Absolutely amazing! Thank you so much!!

Loved it! Clear explanations with simple examples.

Finally i have intuitive view of seld_attention . Thank you😇

This is exceptionally well explained. I'm a student at TU Delft and this really helped me understand how to speed up my code, and why it works. Thanks a lot!

Absolutely amazing series of videos! Congrats!

Very well explained

i dont understand why we have summation in conditionals , should that be a product instead of summation ?

quite bad explanation , just repeating slides text

the approach of the noise filter presented in the video can cause neuron saturation i guess because having high weighted input with maximize the output of the sigmoid function.. which is not desirable

Thanks for your sharing! Nice and clear video!

Currently I am doing research work on TCN

Superb Superb explanation, Superb explanation thanks Superb explanation thanks a Superb explanation thanks a lot! With no predictions now: Wish you all the best and am very grateful for your work.

Why do these videos have ads on them

Google should rank videos according to the likes and the number of previously viewed videos on the same topics: this should go straight to the top for Attention/Transformer searches because I have seen and read plenty, and this is the first time the QKV as dictionary vs RDBMs made sense; that confusion had been so bad it literally stopped me thinking every time I had to consider Q, or K, or V and thus prevented me grokking the big idea. I now want to watch/read everything by you.

Watching this video feels like trying to decipher alien scriptures with a blindfold on.

Waiting for more lectures..

Self attension animationkzbin.info/www/bejne/jabWgnVqa5mkg7ssi=NBxi02yTPzSfMCb6

Hello can someone explain me this, the key and the values for each iteration wont it be the same, as we compare it to 5:29 , please help me on this

Best explanation ever! Congratulations and thank you!

what the dimensions of first conv filter in AlexNet - is it 11x11x 1 or it 11x11x 3 ?

I loved u explanation !!!

You are a great teacher.

Great lecture, thanks!

Really good series of mini-lectures, thanks!

Brilliant content and explanation! It's unfortunate that the fourth part of this lecture series was never published, this realization has left me very sad in 2023. :(

love this two times

Loved this, thanks a lot now I undersand better this things and feel more confident to jump into the code part.

Such a clear explanation! Thank you!

Thanks for the great explanation! Just one question, if simple self-attention has no parameters, how can we expect it to learn? it is not trainable.

Is this ASMR?

good tutorial

Better than the Karpathy explainer video. Enough said!

holy shit, been trying to wrap my head around self-attention for a while, but it all finally clicked together with this video. very well explained, very good video :)