Discover and read the best of Twitter Threads about #GraphNeuralNetworks
Most recents (4)
#GraphNeuralNetworks are way too cool to be left unexplored!
In a nutshell, GNNs are an exciting merger between graph theory (math) & #DeepLearning (coding).
Here's my detailed resource stack of best GNN theory explainers, videos & coding tutorials I used for my own learning.
In a nutshell, GNNs are an exciting merger between graph theory (math) & #DeepLearning (coding).
Here's my detailed resource stack of best GNN theory explainers, videos & coding tutorials I used for my own learning.
1. This is a great place to start if you either: want to learn the basics, or enjoy reading about basic concepts explained in a well structured way.
It walks us through graphs in real world, what graphs & GNNs consist of, and how GNNs do prediction.
distill.pub/2021/gnn-intro/
It walks us through graphs in real world, what graphs & GNNs consist of, and how GNNs do prediction.
distill.pub/2021/gnn-intro/
2. Further, this next tutorial walks us through graphs & GNNs in an intuitive manner, while also going quite deep into the specific mathematical terminology of the field.
I like this one a lot because it also includes hands-on PyTorch code at every step.
theaisummer.com/graph-convolut…
I like this one a lot because it also includes hands-on PyTorch code at every step.
theaisummer.com/graph-convolut…
Me hace muchísima ilusión presentaros el curso de verano que organizamos @andortizg y un servidor para @UNIAuniversidad:
🤖💻🧠
Introducción práctica a la Inteligencia Artificial y el Deep Learning.
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unia.es/inteligencia.a…
#IA #DL #ST
Procedo a vender la moto :)
🤖💻🧠
Introducción práctica a la Inteligencia Artificial y el Deep Learning.
🤖💻🧠
unia.es/inteligencia.a…
#IA #DL #ST
Procedo a vender la moto :)
Inteligencia Artificial… Redes Neuronales…
¡¡LAS MÁQUINAS NOS QUITAN EL TRABAJO!!
Dos campos permean las ciencias en el siglo XXI: la #estadística y la #computación. Combinados, dan lugar al Aprendizaje Automático, el Machine Learning. O como hacer que las máquinas "aprendan"
¡¡LAS MÁQUINAS NOS QUITAN EL TRABAJO!!
Dos campos permean las ciencias en el siglo XXI: la #estadística y la #computación. Combinados, dan lugar al Aprendizaje Automático, el Machine Learning. O como hacer que las máquinas "aprendan"
Como cuento en los monólogos, las máquinas más bien nos quitan "trabajo".
El Machine Learning es herramienta fundamental para automatizar procesos, y hoy en día no se puede entender sin las redes neuronales (la base del Deep Learning #DL).
El Machine Learning es herramienta fundamental para automatizar procesos, y hoy en día no se puede entender sin las redes neuronales (la base del Deep Learning #DL).
The recording of my #inaugural talk at @imperialcollege is now available online
This geometric view on deep learning is the convergence of many old and recent research threads and joint work with @joanbruna @PetarV_93 and @TacoCohen
This geometric view on deep learning is the convergence of many old and recent research threads and joint work with @joanbruna @PetarV_93 and @TacoCohen
Joan's 2014 paper on spectral #graphneuralnetworks was my inspiration to get into this field and write our paper with him, @ylecun @trekkinglemon and Arthur Szlam that popularized the term #geometricdeeplearning
Some of the works I mention are due to my amazing students and colleagues @jonathanmasci Davide Boscaini @EmanueleRodola @befcorreia Pablo Gainza @FreyrSverrisson @emaros96 @ffabffrasca @gbouritsas @frederickmonti @b_p_chamberlain @aittalam Kirill Veselkov Guadalupe Gonzalez
In summer 2020, I started writing a blog on #GraphML in @TDataScience
towardsdatascience.com/graph-deep-lea…
It was a new and rewarding experience, from which I learned a lot. I was surprised that such a technical topic would attract >200K views and >6K claps in less than half a year.
towardsdatascience.com/graph-deep-lea…
It was a new and rewarding experience, from which I learned a lot. I was surprised that such a technical topic would attract >200K views and >6K claps in less than half a year.
I am grateful to all the readers and colleagues whom I used as "guinea pigs" to proof-read my posts especially @frederickmonti @emaros96 @b_p_chamberlain @ffabffrasca @gbouritsas @__lucab @fhuszar and to @kmborgwardt @thomaskipf @guennemann whose comments helped improve them
The first post was based on a seminar I gave for the @IEEEsps on the successes and challenges of #GraphML and #geometricdeeplearning
towardsdatascience.com/deep-learning-…
towardsdatascience.com/deep-learning-…
