fix doc mistakes and broken links. (#271)

* refactor doc

* update with Mao's suggestions

* Set theme jekyll-theme-dinky

* updated the "Contribute" part (merged Gems' wiki in, updated ReadMe)

* fix link

README.md

@@ -81,9 +81,15 @@ The tool dispatches and runs trial jobs that generated by tuning algorithms to s
 * [Serve NNI as a capability of a ML Platform] - *coming soon*
 
 ## **Contribute**
-This project welcomes contributions and suggestions, we are constructing the contribution guidelines, stay tuned =).
+This project welcomes contributions and suggestions, we use [GitHub issues](https://github.com/Microsoft/nni/issues) for tracking requests and bugs.
 
-We use [GitHub issues](https://github.com/Microsoft/nni/issues) for tracking requests and bugs.
+Issues with the **good first issue** label are simple and easy-to-start ones that we recommend new contributors to start with.
+
+To set up environment for NNI development, refer to the instruction: [Set up NNI developer environment](docs/SetupNNIDeveloperEnvironment.md)
+
+Before start coding, review and get familiar with the NNI Code Contribution Guideline: [Contributing](docs/CONTRIBUTING.md)
+
+We are in construction of the instruction for [How to Debug](docs/HowToDebug.md), you are also welcome to contribute questions or suggestions on this area.
 
 ## **License** 
 The entire codebase is under [MIT license](https://github.com/Microsoft/nni/blob/master/LICENSE)

docs/GetStarted.md

@@ -36,7 +36,7 @@ An experiment is to run multiple trial jobs, each trial job tries a configuratio
 
 This command will be filled in the yaml configure file below. Please refer to [here]() for how to write your own trial.
 
-**Prepare tuner**: NNI supports several popular automl algorithms, including Random Search, Tree of Parzen Estimators (TPE), Evolution algorithm etc. Users can write their own tuner (refer to [here](CustomizedTuner.md)), but for simplicity, here we choose a tuner provided by NNI as below:
+**Prepare tuner**: NNI supports several popular automl algorithms, including Random Search, Tree of Parzen Estimators (TPE), Evolution algorithm etc. Users can write their own tuner (refer to [here](howto_2_CustomizedTuner.md), but for simplicity, here we choose a tuner provided by NNI as below:
 
       tuner:
         builtinTunerName: TPE

docs/InstallNNI_Ubuntu.md

@@ -10,8 +10,6 @@
 
     python pip should also be correctly installed. You could use "which pip" or "pip -V" to check in Linux.
 
-    * Note: we don't support virtual environment in current releases.
-
 * __Install NNI through pip__
 
       python3 -m pip install --user nni-pkg

docs/HowToContribute.md → docs/SetupNNIDeveloperEnvironment.md

-**How to contribute**
+**Set up NNI developer environment**
 ===
 ## Best practice for debug NNI source code
 
@@ -51,4 +51,4 @@ After you change some code, just use **step 4** to rebuild your code, then the c
 
 ---
 At last, wish you have a wonderful day.
-For more contribution guidelines on making PR's or issues to NNI source code, you can refer to our [CONTRIBUTING](./docs/CONTRIBUTING.md) document. 
+For more contribution guidelines on making PR's or issues to NNI source code, you can refer to our [CONTRIBUTING](./CONTRIBUTING.md) document. 

docs/howto_2_CustomizedTuner.md

-# Customized Tuner for Experts
+# **How To** - Customize Your Own Tuner
 
 *Tuner receive result from Trial as a matric to evaluate the performance of a specific parameters/architecture configure. And tuner send next hyper-parameter or architecture configure to Trial.*