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Thanks for this, very helpful!

free palestine... the death to Israel

lol they are probably communists

Thank!! nice explanation

Thank you

Thank you, this was a very good explanation!

In the given example, how have we already known that the output matrix is 3*3 and why not 4*4?

Finally understood this topic Thank you 😌😌

thanks bro, was really helpful

2:02 "They are probably communists" 🤣

amazing ,,,got it

That's a very good explanation of deconvolution. Many thanks.

"They're probably communists" LMAO

Simplest and the best explanation. Kindly make more videos.

thanks

Great video, thanks.

red people are probably communists.. lol funny

thanks a lot

"The people are red, probably they are communist"

probably communist🤣

does conv2DTranspose simulate feature x kernel instead of kernel x feature?

Thx J!

2:02 😂

for the example, he wants to transform a 2x2 image to a 4x4. he uses a 3x3. how does he choose the size 3x3? could you choose any size? or is it he only square greater than 2x2 and smaller than 4x4?

Red ppl are communists 🎉

Thanks for the clear explanation!

very good illustrations and to the point explanation

Understoooood! So happy! 😀😀😀 Thank youuuu! Even a bozo like me can understand when taught like this!!! Thank youuuu!

This is the definition of convolution. So what is the difference between Transposed convolution and regular convolution?

Nice to see well made explanations for sub-elements of DL! Clear, short and to the point, thanks!

ty

now that's what i call hand's on explanation! well done!

Bla bla bla 😂

Thanks Joris. Simple and clean

After a lot of texts trying to understand how it works, this video just saved me. Thank you for share the knowledge!

Great explanation. You can improve on your audio Quality however.

Sorry, I am new to this. First time hearing of Transposed Convolution. At 4:17 only one column from each matrix are overlapping. Is it because the stride is 1? If stride is 2, there won't be any overlapping? Thank you. Please be gentle with the reply guys I am a newbie trying to learn.

02:02 what i wasnt expecting that

2:01 Hahaha!!!

Great explanation!

Thanks! This is clear and easy to understand.

Thank you so much!

The wittiest ML guy ever XD. People are in red, maybe because they are probably communists XD @2.04

The official documentation in TensorFlow says "Transposed convolution layer (sometimes called Deconvolution)", but you are saying transpose convolution is not deconvolution. Which is the mistake? www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2DTranspose

ok whats the difference to deconvolution?

okay, that was clear and thank you

Thanks a lot! This helps me understand the UNet's upscaling step a lot better!

Those people in red are probably communistsXD

People are in red. They are probably communists!

you should do another example with a bigger kernel and non-zero padding. Your method is not clear with the example you provided