Update README.md

README.md

@@ -78,7 +78,7 @@ pip install -r requirements.txt
 
 ## Computation requirements
 
-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 problem scale is large, it is advisable to use GPUs.
+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 scale of your task is large, it is advisable to use GPUs if possible.
 
 ## Documentation
 The documenation can be found [here](https://kindxiaoming.github.io/pykan/).