From f3754ce06672bfbf6acd71e90c4da456e70e177a Mon Sep 17 00:00:00 2001 From: Ikko Eltociear Ashimine Date: Thu, 2 May 2024 01:24:51 +0900 Subject: [PATCH] Update README.md documenation -> documentation --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3f123e18..e4500475 100644 --- a/README.md +++ b/README.md @@ -81,7 +81,7 @@ pip install -r requirements.txt Examples in [tutorials](tutorials) are runnable on a single CPU typically less than 10 minutes. All examples in the paper are runnable on a single CPU in less than one day. Training KANs for PDE is the most expensive and may take hours to days on a single CPU. We use CPUs to train our models because we carried out parameter sweeps (both for MLPs and KANs) to obtain Pareto Frontiers. There are thousands of small models which is why we use CPUs rather than GPUs. Admittedly, our problem scales are smaller than typical machine learning tasks, but are typical for science-related tasks. In case the scale of your task is large, it is advisable to use GPUs. ## Documentation -The documenation can be found [here](https://kindxiaoming.github.io/pykan/). +The documentation can be found [here](https://kindxiaoming.github.io/pykan/). ## Tutorials