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I don't know why this tutorial is underrated.. you've explained everything in a very understandable way.. 💫🤞🏻
Thank you for your introduction ! This is actually the first course I see introducing 1D-CNN before 2D-CNN. It is an interesting perspective !
Amazing video really! Thank you much, you beautifully explained a lot in a very short time!
Very well explained! Thanks for the content!
excellent lecture, thanks for sharing!
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
Do you have some implementations of conv 1d in keras or pytorch?
I don't know why this tutorial is underrated.. you've explained everything in a very understandable way.. 💫🤞🏻
Thank you for your introduction ! This is actually the first course I see introducing 1D-CNN before 2D-CNN. It is an interesting perspective !
Amazing video really! Thank you much, you beautifully explained a lot in a very short time!
Very well explained! Thanks for the content!
excellent lecture, thanks for sharing!
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
Do you have some implementations of conv 1d in keras or pytorch?