PyTorch 2D Convolution

In this video, we cover the input parameters for the PyTorch torch.nn.Conv2d module.
VIDEO CHAPTERS
0:00 Introduction
0:37 Example
2:46 torch.nn.Conv2d
3:28 Input/Output Channels
4:40 Kernel
5:15 Stride
6:17 Padding
8:01 Dilation
9:04 Groups
11:13 Bias
11:50 Output Shape

[@anoushkagade8091]

Thank you so much for this explanation! I have been looking everywhere, and most tutorials assume that the viewer has a grasp on these concepts. Finally found a good explanation!

[@raneisenberg2155]

Thank you very much for the positive feedback :)

[@balintfurmann5560]

Great video, made me understand the CNN-s in Python much better. Thank you!

[@andrefurlan]

Thank you a lot! I finally understood from where all these filters comes!

[@hamidfallah9223]

perfect esplanation, straight foward and pure informative. tnx.

[@oguzhanoguz621]

great video, thank you so much. this video is worth more than 100 wiki pages

[@user-tn9ol6wb4z]

Super clear explaination. Thanks !!!

[@Gan-tingLoo]

Thanks for the video! I believe for the group example, the input sample is (8x5x5) instead of (8x7x7)

[@Qdkdj]

Thank you！

[@liuzq7]

The best explanation. Thank you.

[@gogaworm]

Very good explanation, thank you. Please continue to make videos.

[@aaomms7986]

Thank you this is the best explanation ❤❤❤

[@user-xm3mb6dj2g]

you goated for this one fam

[@chrisseberino]

Very good!

[@hirotrex42]

Thank you !

[@Commenterrist]

thx

[@user-kn1we2kp3m]

Thank you. One of the best and clearest explanations I have seen without wading through a ton of documentation.

[@adityategar6178]

My input always read (N, Hin, Win, Cin). How can i fix it to (N, Cin, Hin, Win)?

[@brucemurdock5358]

What if I change the kernel size and want to keep my input size? Would padding still be 1?