added info about playing_with_outliers app

Author: dmclark53

README.md

@@ -1,11 +1,17 @@
 # Introduction to TensorFlow
-In this tutorial the steps needed to clean a dataset and prepare it for modeling using the machine learning library TensorFlow. The tutorial uses the [Wine](http://archive.ics.uci.edu/ml/datasets/Wine) dataset from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml).
+In this tutorial the steps needed to clean a dataset and prepare it for modeling using the machine learning library
+TensorFlow. The tutorial uses the [Wine](http://archive.ics.uci.edu/ml/datasets/Wine) dataset from the
+[UCI Machine Learning Repository](http://archive.ics.uci.edu/ml).
 
 ## Prerequesits
-This tutorial includes several machine learning terms. To get a good mathematical understanding of these concepts, please read the Math Primer.
+This tutorial includes several machine learning terms. To get a good mathematical understanding of these concepts,
+please read the Math Primer.
 
 ## Installation Notes
-There are a few packages you will need in order to run this tutorial. We recommend installing the miniconda environment, which makes the installation process quite easy. Please see the [README](https://github.com/PythonWorkshop/intro-to-sklearn) file for this mornings session for instructions on how to install miniconda.
+There are a few packages you will need in order to run this tutorial. We recommend installing the miniconda environment,
+which makes the installation process quite easy. Please see the
+[README](https://github.com/PythonWorkshop/intro-to-sklearn) file for this mornings session for instructions on how to
+install miniconda.
 
 In order to run this tutorial, you will need the following Python packages:
 * numpy
@@ -29,8 +35,27 @@ Alternatively if you are using conda you can do:
 conda install numpy pandas seaborn scikit-learn
 ```
 
-For TensorFlow, the installation depends on your environment. Below are installation instructions. For detailed instuctions, please see the TensorFlow [README](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md) file.
+For TensorFlow, the installation depends on your environment. Below are installation instructions. For detailed
+instuctions, please see the TensorFlow
+[README](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md) file.
 
 ### NOTE:
 What's better to use? The virtual environment or normal pip installation?
 
+## Play with outliers
+
+I have added a fun interactive application using the Python visualization library called Bokeh. The app allows you to
+pick features from the wine data set and set an outlier threshold to explore how this affects the data. The application
+source code is in the `playing_with_outliers` directory and is called `main.py`. To run this application, you will need
+to install bokeh:
+
+```
+pip install bokeh
+```
+
+Then, to run the application, download the `playing_with_outliers` directory. Then, in the directory where you downloaded
+it, run:
+
+```
+bokeh serve --show playing_with_outliers
+```