[docs] update doc website

Author: Rhett-Ying

README.md

@@ -9,7 +9,7 @@
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](./LICENSE)
 [![Twitter](https://img.shields.io/twitter/follow/DGLGraph?style=social)](https://twitter.com/GraphDeep)
 
-[Website](https://www.dgl.ai) | [A Blitz Introduction to DGL](https://docs.dgl.ai/tutorials/blitz/index.html) | Documentation ([Latest](https://docs.dgl.ai/en/latest/) | [Stable](https://docs.dgl.ai)) | [Official Examples](examples/README.md) | [Discussion Forum](https://discuss.dgl.ai) | [Slack Channel](https://join.slack.com/t/deep-graph-library/shared_invite/zt-eb4ict1g-xcg3PhZAFAB8p6dtKuP6xQ)
+[Website](https://www.dgl.ai) | [A Blitz Introduction to DGL](https://docs.dgl.ai/tutorials/blitz/index.html) | Documentation ([Latest](https://www.dgl.ai/dgl_docs/) | [Official Examples](examples/README.md) | [Discussion Forum](https://discuss.dgl.ai) | [Slack Channel](https://join.slack.com/t/deep-graph-library/shared_invite/zt-eb4ict1g-xcg3PhZAFAB8p6dtKuP6xQ)
 
 DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow.