The center of the technology world may not lie in California's Silicon Valley, but in the bustling marketplace of Huaqiangbei, a subdistrict of Shenzhen...
Spinning objects have strange instabilities known as The Dzhanibekov Effect or Tennis Racket Theorem - this video offers an intuitive explanation. Part of th...
Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2019 Notebook: http://deeplearning.cs.cmu.edu/document/recitation/recitation...
This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN) and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras.
DownSub is a free web application that can download subtitles directly with playlist from Youtube, Drive, Viu, Vimeo, Viki, OnDemandKorea, Vlive and more.
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
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.
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
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/