EPISODE · Dec 12, 2018 · 10 MIN
Geometric Deep Learning
from Siraj Raval
Geometric Deep Learning is able to draw insights from graph data. That includes social networks, sensor networks, the entire Internet, and even 3D Objects (if we consider point cloud data to be a graph). I'll explain how it works via a demo of me using a graph convolutional network to classify people by their interest in sports teams as well as a 3D object classification demo. At its core, it comes down to being able to learn from non-Euclidean data. Euclid's laws help define certain types of data, so I'll cover some geometry background as well. Enjoy! Code for this video: https://github.com/llSourcell/pytorch_geometric Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: http://sungsoo.github.io/2018/02/01/geometric-deep-learning.html http://geometricdeeplearning.com/ https://arxiv.org/abs/1611.08097 http://3ddl.stanford.edu/CVPR17_Tutorial_Intrinsic_CNNs_compressed.pdf https://github.com/rusty1s/pytorch_geometric https://pemami4911.github.io/paper-summaries/deep-learning-theory/2017/11/19/geometric-deep-learning.html Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/ Need help on a project? See our consulting group: https://www.theschool.ai/consulting-group/
What this episode covers
Geometric Deep Learning is able to draw insights from graph data. That includes social networks, sensor networks, the entire Internet, and even 3D Objects (if we consider point cloud data to be a graph). I'll explain how it works via a demo of me using a graph convolutional network to classify people by their interest in sports teams as well as a 3D object classification demo. At its core, it comes down to being able to learn from non-Euclidean data. Euclid's laws help define certain types of data, so I'll cover some geometry background as well. Enjoy! Code for this video: https://github.com/llSourcell/pytorch_geometric Please Subscribe! And Like. And comment. Thats what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: http://sungsoo.github.io/2018/02/01/geometric-deep-learning.html http://geometricdeeplearning.com/ https://arxiv.org/abs/1611.08097 http://3ddl.stanford.edu/CVPR17_Tutorial_Intrinsic_CNNs_compressed.pdf https://github.com/rusty1s/pytorch_geometric https://pemami4911.github.io/paper-summaries/deep-learning-theory/2017/11/19/geometric-deep-learning.html Join us at the School of AI: https://theschool.ai/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: https://www.theschool.ai/jobs/ Need help on a project? See our consulting group: https://www.theschool.ai/consulting-group/
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Geometric Deep Learning
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