Requirements:
https://challenges.robotevents.com/challenge/228/REC-Foundation-VR-Challenge
Code Online:
Ranking:
https://www.robotevents.com/robot-competitions/vex-iq-competition/virtualSkillsStandings
Winners:
https://challenges.robotevents.com/challenge/228/entry?filter=winners
Introduction and experience:
https://www.youtube.com/watch?v=I1xfAxlaTgo
These terms usually confuse the beginners. Let's explain them in a simple and clear way.
CUDA vs cuDNN:
NVIDIA CUDA Deep Neural Network (cuDNN) is a GPU-accelerated library of primitives for deep neural networks.
The cuDNN library is a library optimized for CUDA containing GPU implementations. Think of cuDNN as a library for Deep Learning using CUDA and CUDA as a way to talk to the GPU.
In summary, CUDA provides the programming interface and runtime environment for general-purpose computing on a GPU, while cuDNN is a library that specifically optimizes deep learning operations on a GPU.
tiny-cuda-nn:
tiny-cuda-nn comes with a PyTorch extension that allows using the fast MLPs and input encodings from within a Python context. These bindings can be significantly faster than full Python implementations; in particular for the multi-resolution hash encoding.
Yellow dispenser:
https://www.youtube.com/watch?v=hAXIjDPQzdg&t=10s
https://www.youtube.com/watch?v=vx8JFi54sfo&t=26321s
