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AMMI Course "Geometric Deep Learning" - Lecture 1 (Introduction) - Michael Bronstein

Кадры AMMI Course "Geometric Deep Learning" - Lecture 1 (Introduction) - Michael Bronstein

Описание AMMI Course "Geometric Deep Learning" - Lecture 1 (Introduction) - Michael Bronstein

Коментарии AMMI Course "Geometric Deep Learning" - Lecture 1 (Introduction) - Michael Bronstein

The portrait on the left @5:35 is Pierre de Fermat and it says Desargues 😅 Comment from : @Alejandro-hh5ub

polish accent Comment from : @AtticusDenzil

banger course Comment from : @vaap

Excellent Lecture Thanks and appreciate it Comment from : @channagirijagadish1201

Hi, I can't access the slide When I clicked on it, it states that This URL has been blocked by Bitly's systems as potentially harmful Comment from : @mingmingtan8790

The link to the lecture slides is not working, is there anyway to still access them? Comment from : @syedakbari845

Bravo Michael! I really love that you put things into a historical context - that helps us create a map (a graph :) ) of how concepts connect and evolve and by introducing this structure into our mental models it's easier to explore this vast space of knowledge Comment from : @TheAIEpiphany

32:45 that approach is too naive If I say "I hate nachos", it doesn't mean that I have a connection with every nacho past-present-future and I hate every single one of them uniquely No! I just hate nachos After 1 minute of thinking you can realize what you need is hypergraphs in almost every situation Comment from : @444haluk

I didnt understand most things mentioned here hopefully the later lectures make provide more insight Comment from : @randalllionelkharkrang4047

28:32 - A moment of revelation! wow!🤯 Comment from : @rock_it_with_asher

Thank you Dr Bronstein for the extraordinary introductory lecture Really excited to go through the rest of the lectures in this series! I have 2 questions based on the introduction:br1) When discussing the MNIST example you mentioned that images are high dimensional Could not understand that point as generally the images such as the MNIST dataset are considered to be 2-dimensional in other general DL/CNN courses Can you elaborate more on how the higher dimensions emerge or how to visualize those for cases such as the MNIST dataset?br2) In case of molecules, even though the order of nodes can vary, the neighborhood of each node remains the same under non-reactive conditions (when bond formation/breakage is not expected) In such cases, does permutation invariance only mean the order in which nodes are traversed in the graph (Like variations in atom numbering between IUPAC names of molecules)? Does permutation invariance take into account changes in node neighborhood?brI apologize for the naive questions professor Thank you once again for the initiative to digitize these lectures for the benefit of students and researchers Comment from : @sowmyakrishnan240

The very fact that the human brain is captivated and fascinated by manifolds is enough to prove that the brain does not use the concept of manifolds in any manner I'm going to tell you a scenery secret I happen to know: The brain is purely an sparse-L1 norm processor It has no notion of "distance" except in the form of pattern matchingbrbrYou're welcome so now you can throw this entire video and all related research in the garbage, unless your goal is to make something better than the human brain Comment from : @JohnSmith-ut5th

Amazing lecture However, I was not able to understand the mathematical part Can someone suggest to me the prerequisites for this lecture series? Comment from : @akshaysarbhukan6701

Awesome Video ! Comment from : @sumitlahiri4973

This is truly amazing I finished my bachelor in mathematics, with a thesis in differential geometry, and I just started studying a masters degree in Artificial Intelligence Research I saw some articles on geometric deep learning, but nothing as complete as this I think this beautiful field fits my interests perfectly and I think I'll orient my research career in this direction Thank you very much for this Comment from : @UberRobVlogs

Great work, thanks Comment from : @droidcrackye5238

Amazing lecture series! Comment from : @NoNTr1v1aL

today I got the book that Dr Bronstein suggested "The Road to Reality" by Roger Penrosewow I wish that I had came across this book wayyy earlier If I had this when I was in early undergraduate I would had much much more fun and motivation to study physics and mathematics This is just amazing Comment from : @fredxu9826

I just started reading your book "Numerical geometry " today out of curiosity and this shows up on youtube I'm looking forward to learning something new 🙂 Comment from : @jordanfernandes581

Thank you! Comment from : @jobiquirobi123

This will keep me busy for the next few weeks!! Comment from : @gowtham236

thanks for uploading this ! Comment from : @maximeg3659

I had seen your previous ICLR presentation on the same topic and was still not clear about the invariance and equivariance ideas! Now finally I got hold of the concept of inductive biases (geometric priors) that must be ensured for model architecturesbr1 images - shift inv and equivbr2 graphs - premutation inv and equivbr3 sequences/language - ??brand for any other tasks we may encounter - we need to identify which property wrt the resulting function should be invariant and equivariant! Thank you very much Sir for generously putting it all out there for the public good Comment from : @samm9840

Thank you for uploading this Comment from : @Chaosdude341

05:08 Desargues looks strikingly similar to Pierre de Fermat I think one of them is wrong Comment from : @xinformatics

Is there any book that correlates with geometric deep learning course presented in this course? Comment from : @mlworks

Oh my God thank you very much for your effort Comment from : @abdobrahany8236

This is just pure coincidence I'm currently interested in this topic and this amazing course poped up Thank you very much Prof Michael for opening these resources to the public I might try to get in touch with you or your colleagues to discuss some ideas Regards! M Saval Comment from : @marfix19

Oh wow! Ties together so many areas I’ve been interested over the years - with concrete, intuitive, applications Comment from : @petergoodall6258

Awesome Thanks! Comment from : @krishnaaditya2086

What a good time to be alive! I’m going to enjoy this playlist Comment from : @fredxu9826

Hi Dear Professor Michael Bronstein, Congratulations for the great job you and your team are doing in the field of AI Im going to my junior year at university and kinda failed in love with the Goemetric deep learning Hopefully these lesson and the paper will help me to understand more about Thanks for sharing, All the best Comment from : @edsoncasimiro

Oh, my GOD!! I expect from this course nothing short of pure awesomeness Bless you, Professor Bronstein! Comment from : @georgy8335

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