Deep Learning 59: Fundamentals of Graph Neural Network 

One of the best professors on the planet

No BS and straight to the point.. Thanks for this gem

Very helpful tutorial. Please include Graph Convolution neural network too. Thanks!

Great explanation sir....

Great. Hope you make more video about graph deep learning

These lectures are fantastic. Thank you so much sir. Just one request there are too much adverts in between and these adverts literally throw my concentration off everytime...

Thank you for this great series on GNNs, what do you recommend as a paper to learn more variants of GNNs, I found dozens in the literature and I am confused about which one is the next. Greetings from Algeria.

Excellent knowledge transfer. Thank you.

Great and to the point explanation sir. Can you make more videos from basics to advanced in graph neural network. If you have prepared where can I find the proper playlist on the same.

Excellent!

Very very helpful. Thanks a lot!

Great video sir, Can you please explain @15:46 why did you only take self loop for node 1 only and not others ?

Could I understand that all the graph neural networks are designed to achieve one sole purpose, which is calculating the node/vertex embedding?

Very nicely explained 👍

Nice and clear explanation!, weating the next video especially normalization and propagation rule

thanks , clear explanation we need the implamatation pls to more understanding

What about graphs that have both directed and undirected edges? What are they called? That's actually quite common - a street network that has some two-way and some one-way streets is both.

First think that came to my mind: Neural Nets itself are graphs. MINDBLOWN

Hi sir it will be good if you can provide some problem sets so that we can learn by doing some problems. Thanks

This is very useful. But I counted an Ad every 1 minute of watching. This is really frustrating.

Thank you Sir, Can you make a Video on CPVTON, GMM or Face Reconstruction?

When is the next video coming up for this? eagerly waiting.

Thank you so much for such a nice tutorial. kindly if you have any tutorial on EBGAN or Energy related GAN please share!!!! much appreciated.

Sir, can you make videos on DTW, GMM and HMM?

Thank you so much. I'll be waiting for the next topic.

Nice lecutre :-)

Hmm. How does this not cover message passing and convolution?

thanks sir

Nice person. Waiting for more videos.

My question is not related to this video .. Sir , firstly how to create weight file after training the neural network ? Second , how we can use this weighted file on our local pc ? Plz provide some related link/contant , make video on this....

Good video with some verbal mistakes like: unique/different 5:57, the word unique and different means the same . Initially in the first part numbers of nodes were miscalculated. Overall appreciated!

Thanks

Great explanation! Just want to point out that being unique means the same as two or more things being different. I think there some confusion here 6:10.

How 1 and 1 are unique 6:11

Honey lecture series

You say unique but you mean non-unique, right?

Ridiculous amount of ads thrown into the video

IT IS NOT YET NEURAL NETWORK ! YOU ARE TALKING ABOUT GRAPH THEORY ONLY !!!

why are there dislikes on the video ? Where do these people come from, what do they need ?

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