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docs: Update interpreter binding mode page

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docs/usage/interpreter/interpreter_binding_mode.md
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@ -25,4 +25,73 @@ limitations under the License.
## Overview
Interpreter is a JVM process that communicates to Zeppelin daemon using thrift.
Each Interpreter process can have Interpreter Groups, and each interpreter instance belongs to this Interpreter Group.
(See [here](../../development/writing_zeppelin_interpreter.html) to understand more about its internal structure.)
Zeppelin provides 3 different modes to run interpreter process: **shared**, **scoped** and **isolated**.
These 3 modes give flexibility to fit Zeppelin into any type of use cases.
## Shared Mode
<div class="text-center">
<img src="/assets/themes/zeppelin/img/docs-img/interpreter_binding_mode-shared.png">
</div>
<br/>
In **Shared** mode, single JVM process and single Interpreter Group serves all Notes.
## Scoped Mode
<div class="text-center">
<img src="/assets/themes/zeppelin/img/docs-img/interpreter_binding_mode-scoped.png">
</div>
<br/>
In Scoped mode, Zeppelin still runs single interpreter JVM process but multiple Interpreter Group serve each Note.
So, each Note have their own dedicated session but still it’s possible to share objects between different Interpreter Groups while they’re in the same JVM process.
## Isolated Mode
<div class="text-center">
<img src="/assets/themes/zeppelin/img/docs-img/interpreter_binding_mode-shared.png">
</div>
<br/>
**Isolated** mode runs separate interpreter process for each Note. So, each Note have absolutely isolated session.
## Which mode should I use?
Each Interpreter implementation may have different characteristics depending on the back end system that they integrate. And 3 interpreter modes can be used differently.
Let’s take a look how Spark Interpreter implementation uses these 3 interpreter modes, as an example.
Spark Interpreter implementation includes 4 different interpreters in the group: Spark, SparkSQL, Pyspark and SparkR.
SparkInterpreter instance embeds Scala REPL for interactive Spark API execution.
<br/>
In Shared mode, a SparkContext and a Scala REPL is being shared among all interpreters in the group.
So every Note will be sharing single SparkContext and single Scala REPL.
In this mode, if NoteA defines variable ‘a’ then NoteB not only able to read variable ‘a’ but also able to override the variable.
<div class="text-center">
<img src="/assets/themes/zeppelin/img/docs-img/interpreter_binding_mode-example-spark-shared.png">
</div>
<br/>
In Scoped mode, each Note has its own Scala REPL.
So variable defined in a Note can not be read or overridden in another Note.
However, still single SparkContext serves all the Interpreter Groups.
And all the jobs are submitted to this SparkContext and fair scheduler schedules the job.
This could be useful when user does not want to share Scala session, but want to keep single Spark application and leverage its fair scheduler.
<div class="text-center">
<img src="/assets/themes/zeppelin/img/docs-img/interpreter_binding_mode-example-spark-scoped.png">
</div>
<br/>
In Isolated mode, each Note has its own SparkContext and Scala REPL.
<div class="text-center">
<img src="/assets/themes/zeppelin/img/docs-img/interpreter_binding_mode-example-spark-isolated.png">
</div>
<br/>