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README.md

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 # Kolmogorov-Arnold Newtworks (KANs)
 
-This the github repo for the paper "KAN: Kolmogorov-Arnold Networks" [link]. The documentation can be found here [link].
+This the github repo for the paper "KAN: Kolmogorov-Arnold Networks" [link]. Find the [documentaion here](https://kindxiaoming.github.io/pykan/).
 
 Kolmogorov-Arnold Networks (KANs) are promising alternatives of Multi-Layer Perceptrons (MLPs). KANs have strong mathematical foundations just like MLPs: MLPs are based on the [universal approximation theorem](https://en.wikipedia.org/wiki/Universal_approximation_theorem), while KANs are based on [Kolmogorov-Arnold representation theorem](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Arnold_representation_theorem). KANs and MLPs are dual: KANs have activation functions on edges, while MLPs have activation functions on nodes. This simple change makes KANs better (sometimes much better!) than MLPs in terms of both model accuracy and interpretability. 
 
@@ -43,16 +43,16 @@ pip install -r requirements.txt
 ```
 
 ## Documentation
-The documenation can be found here [].
+The documenation can be found [here](https://kindxiaoming.github.io/pykan/).
 
 ## Tutorials
 
 **Quickstart**
 
-Get started with [hellokan.ipynb](./hellokan.ipynb) notebook
+Get started with [hellokan.ipynb](./hellokan.ipynb) notebook.
 
 **More demos**
 
-Jupyter Notebooks in [docs/Examples](./docs/Examples) and [docs/API_demo](./docs/API\_demo) are ready to play. You may also find these examples in documentation.
+More Notebook tutorials can be found in [tutorials](tutorials).