Update contributing.rst

Author: zsdonghao

docs/user/contributing.rst

@@ -4,12 +4,10 @@
 Contributing
 ===============
 
-TensorLayer 2.0 is a major ongoing research project in CFCS, Peking Univeristy, and the first version was established at Imperial College in 2016.
-The goal of the project is to develop a compositional language while complex learning systems
+TensorLayer 2.0 is a major ongoing research project in CFCS, Peking University, the first version was established at Imperial College London in 2016. The goal of the project is to develop a compositional language while complex learning systems
 can be built through composition of neural network modules.
 
-Numerous contributors come from various horizons such as: Imperial College, Tsinghua University, Carnegie Mellon University, University of Technology of Compiegne, 
-Google, Microsoft, Bloomberg and etc.
+Numerous contributors come from various horizons such as: Imperial College London, Tsinghua University, Carnegie Mellon University, Stanford, University of Technology of Compiegne, Google, Microsoft, Bloomberg and etc.
 
 You can easily open a Pull Request (PR) on `GitHub`_, every little step counts and will be credited.
 As an open-source project, we highly welcome and value contributions!
@@ -25,7 +23,6 @@ As an open-source project, we highly welcome and value contributions!
 Project Maintainers
 --------------------------
 
-
 The TensorLayer project was started by `Hao Dong <https://zsdonghao.github.io>`_ at Imperial College London in June 2016. 
 
 For TensorLayer 2.x, it is now actively developing and maintaining by the following people who has more than 50 contributions:
@@ -54,10 +51,9 @@ What to contribute
 Your method and example
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-If you have a new method or example in terms of Deep learning and Reinforcement learning,
-you are welcome to contribute.
+If you have a new method or example in terms of Deep learning or Reinforcement learning, you are welcome to contribute.
 
-* Provide your layer or example, so everyone can use it.
+* Provide your layers or examples, so everyone can use it.
 * Explain how it would work, and link to a scientific paper if applicable.
 * Keep the scope as narrow as possible, to make it easier to implement.