Convolution, Kernels and Filters - Visually Explained + PyTorch/numpy code | Essentials of ML

This tutorial explains (provide proofs using code) the components & operations in a convolutional layer in neural networks.
The difference between Kernel and Filter is clarified as well.
The tutorial also points out that not all kernels convolve/correlate with all input channels. This seems to be a common misunderstanding for many people. Hopefully, this visual and code example can help show these aspects.
Google colab notebook:
colab.research...
Playlist:
• Let's make the Correla...
#cnn
#signalprocessing
#deeplearning

[@mabelgonzalezcastellanos3442]

Very clear explanation, thanks!

[@KapilSachdeva]

🙏

[@josephchan9414]

Thank you!!!!

[@KapilSachdeva]

🙏

[@quantum01010101]

Great, Thank you

[@KapilSachdeva]

🙏

[@quantum01010101]

@@KapilSachdeva
An example of using MH for multivariate problem would be of great value.

[@KapilSachdeva]

MH? Do u mean Metropolis Hastings?

[@quantum01010101]

@@KapilSachdeva Yes

[@fairuzshadmanishishir8171]

Please make videos on graph neural network

[@KapilSachdeva]

🙏 …will try!