Uploaded May 2021 | Updated September 2026, 2 hours ago
GitHub link: gist.github.com/ejmejm/1baeddbbe48f58dbced9c019c25ebf71
Here are 7 tips for improving your PyTorch skills. These are all things that I thought of because I use on a normal basis. PyTorch has a lot of need things you can do with modeling to distributions, let me know other tips you have in the comments below!
Tips:
1. Create tensors directly on the target device
2. Use Sequential layers when possible
3. Don't make lists of layers
4. Make use of distributions
5. Use the detach method when the gradient is not needed
6. How to delete a model from the GPU
7. Call the eval method before testing
GitHub link: gist.github.com/ejmejm/1baeddbbe48f58dbced9c019c25ebf71
Here are 7 tips for improving your PyTorch skills. These are all things that I thought of because I use on a normal basis. PyTorch has a lot of need things you can do with modeling to distributions, let me know other tips you have in the comments below!
Tips:
1. Create tensors directly on the target device
2. Use Sequential layers when possible
3. Don't make lists of layers
4. Make use of distributions
5. Use the detach method when the gradient is not needed
6. How to delete a model from the GPU
7. Call the eval method before testing
![Self-Supervised RL - Learning Without Data [Zero to Paper]](https://i.ytimg.com/vi/CDKsa06xU0o/mqdefault.jpg "Self-Supervised RL - Learning Without Data [Zero to Paper]
Inverse Reinforcement Learning with Natural Language Goals (LangGoal IRL) offers a way to do sample-efficient IRL and a way to generalize using self-supervised learning. The paper is novel and is a step forward for general AI and ML algorithms. Though it has its cons, I think it is one of the better papers out there that cover RL, IRL, NLP, and generalization.
Zero to Paper playlist: https://www.youtube.com/playlist?list=PL_49VD9KwQ_ONxENRk11jFEI3_pqAwaug
Inverse Reinforcement Learning video: https://www.youtube.com/watch?v=qo355ALvLRI
Paper covered: https://arxiv.org/pdf/2008.06924.pdf")
