[Doc] Revise tooltips on decision tree and small fixes (#6197)
* Revise tooltips of decision tree * Small list indent problem fix
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			@ -3,38 +3,38 @@ With the summary info generated, we can then use [TensorBoard](https://pypi.pyth
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* **Installing TensorBoard**
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Prerequisites:
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  Prerequisites:
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1. Python verison: 2.7, 3.4, 3.5, or 3.6
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2. Pip version >= 9.0.1
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  1. Python verison: 2.7, 3.4, 3.5, or 3.6
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  2. Pip version >= 9.0.1
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To install TensorBoard using Python 2, you may run the command:
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```bash
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pip install tensorboard==1.0.0a4
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```
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     To install TensorBoard using Python 2, you may run the command:
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     ```bash
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     pip install tensorboard==1.0.0a4
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     ```
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To install TensorBoard using Python 3, you may run the command:
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```bash
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pip3 install tensorboard==1.0.0a4
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```
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     To install TensorBoard using Python 3, you may run the command:
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     ```bash
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     pip3 install tensorboard==1.0.0a4
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     ```
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Please refer to [this page](https://github.com/intel-analytics/BigDL/tree/master/spark/dl/src/main/scala/com/intel/analytics/bigdl/visualization#known-issues) for possible issues when installing TensorBoard.
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     Please refer to [this page](https://github.com/intel-analytics/BigDL/tree/master/spark/dl/src/main/scala/com/intel/analytics/bigdl/visualization#known-issues) for possible issues when installing TensorBoard.
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* **Launching TensorBoard**
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You can launch TensorBoard using the command below:
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```bash
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tensorboard --logdir=/tmp/bigdl_summaries
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```
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After that, navigate to the TensorBoard dashboard using a browser. You can find the URL in the console output after TensorBoard is successfully launched; by default the URL is http://your_node:6006
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  You can launch TensorBoard using the command below:
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  ```bash
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  tensorboard --logdir=/tmp/bigdl_summaries
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  ```
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  After that, navigate to the TensorBoard dashboard using a browser. You can find the URL in the console output after TensorBoard is successfully launched; by default the URL is http://your_node:6006
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* **Visualizations in TensorBoard**
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Within the TensorBoard dashboard, you will be able to read the visualizations of each run, including the “Loss” and “Throughput” curves under the SCALARS tab (as illustrated below):
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  Within the TensorBoard dashboard, you will be able to read the visualizations of each run, including the “Loss” and “Throughput” curves under the SCALARS tab (as illustrated below):
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And “weights”, “bias”, “gradientWeights” and “gradientBias” under the DISTRIBUTIONS and HISTOGRAMS tabs (as illustrated below):
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  And “weights”, “bias”, “gradientWeights” and “gradientBias” under the DISTRIBUTIONS and HISTOGRAMS tabs (as illustrated below):
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---
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			@ -18,21 +18,22 @@ Choosing the right BigDL library
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.. graphviz::
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    digraph BigDLDecisionTree {
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        graph [pad=0.1 ranksep=0.3]
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        node [color="#0171c3" shape=box fontname="Arial" fontsize=14]
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        graph [pad=0.1 ranksep=0.3 tooltip=" "]
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        node [color="#0171c3" shape=box fontname="Arial" fontsize=14 tooltip=" "]
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        edge [tooltip=" "]
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        Feature1 [label="Hardware Secured Big Data & AI?"]
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        Feature2 [label="Python vs. Scala/Java?"]
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        Feature3 [label="What type of application?"]
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        Feature4 [label="Domain?"]
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        Orca[href="../doc/Orca/index.html" target="_blank" target="_blank" style="rounded,filled" fontcolor="#ffffff"]
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        Nano[href="../doc/Nano/index.html" target="_blank" target="_blank" style="rounded,filled" fontcolor="#ffffff"]
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        DLlib1[label="DLlib" href="../doc/DLlib/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff"]
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        DLlib2[label="DLlib" href="../doc/DLlib/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff"]
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        Chronos[href="../doc/Chronos/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff"]
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        Friesian[href="../doc/Friesian/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff"]
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        PPML[href="../doc/PPML/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff"]
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        Orca[href="../doc/Orca/index.html" target="_blank" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-Orca document"]
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        Nano[href="../doc/Nano/index.html" target="_blank" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-Nano document"]
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        DLlib1[label="DLlib" href="../doc/DLlib/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-DLlib document"]
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        DLlib2[label="DLlib" href="../doc/DLlib/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-DLlib document"]
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        Chronos[href="../doc/Chronos/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-Chronos document"]
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        Friesian[href="../doc/Friesian/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-Friesian document"]
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        PPML[href="../doc/PPML/index.html" target="_blank" style="rounded,filled" fontcolor="#ffffff" tooltip="Go to BigDL-PPML document"]
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        ArrowLabel1[label="No" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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        ArrowLabel2[label="Yes" fontsize=12 width=0.1 height=0.1 style=filled color="#c9c9c9"]
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