Spheres are nice and all, but there comes a time when more complex shapes are needed. One popular algorithm for testing collisions is the Gilbert–Johnson–Keerthi algorithm, or GJK for short. With it we can detect collisions between any two convex polygons.
Check out the full article: https://blog.winter.dev/2020/gjk-algorithm/
Physics is a part of games that has always amazed me. I find it funny how impossible it seemed to do correctly when I was younger. While making a custom game engine, it was finally demystified!
The full article: https://blog.winter.dev/2020/designing-a-physics-engine/
The background game demo: https://winter.dev/demo
In this video we will take an in depth look at the fast inverse square root and see where the mysterious number 0x5f3759df comes from. This algorithm became famous after id Software open sourced the engine for Quake III. On the way we will also learn about floating point numbers and newton's method.
This is the Graph Neural Networks: Hands-on Session from the Stanford 2019 Fall CS224W course.
In this tutorial, we will explore the implementation of graph neural networks and investigate what representations these networks learn. Along the way, we'll see how PyTorch Geometric and TensorBoardX can help us with constructing and training graph models.
Pytorch Geometric tutorial part starts at -- 0:33:30
* Graph Convolutional Neural Networks (GCN)
* Custom Convolutional Model
* Message passing
* Aggregation functions
* Graph Pooling