This archive holds videos of past Fields lectures. Lectures are archived in two formats.The interactive format, viewed in a flash-player-enabled desktop web browser, allows you to zoom in and out on specific areas of the blackboards or screens (providing a viewing experience more like being present in the room). The static format, although it does not allow for zooming in to read small blackboard writing, is downloadable and compatible with a wide variety of desktop and mobile video players.
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
Details on:
* Graph Convolutional Neural Networks (GCN)
* Custom Convolutional Model
* Message passing
* Aggregation functions
* Update
* Graph Pooling
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
What if there was an Empire-focussed short Star Wars animation, drawn with the crazy detail and shading of classic 80s anime that's all but vanished from Japan nowadays? Well, I tried my best.
The textbook Analytic Combinatorics by Philippe Flajolet and Robert Sedgewick enables precise quantitative predictions of the properties of large combinatorial structures.
Karan Thapar speaks to former Pakistani ambassador to the US and well-known academic scholar Husain Haqqani about his new book 'Reimagining Pakistan: Transfo...