softmax regression bonus notebook

README.md

@@ -90,6 +90,7 @@ Simply click on the `ipynb`/`nbviewer` links next to the chapter headlines to vi
 - A Simple Barebones Flask Webapp Template [[view directory](./code/bonus/flask_webapp_ex01)][[download as zip-file](https://github.com/rasbt/python-machine-learning-book/raw/master/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip)]
 - Reading handwritten digits from MNIST into NumPy arrays [[GitHub ipynb](./code/bonus/reading_mnist.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/reading_mnist.ipynb)]
 - Scikit-learn Model Persistence using JSON [[GitHub ipynb](./code/bonus/scikit-model-to-json.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/scikit-model-to-json.ipynb)]
+- Multinomial logistic regression / softmax regression [[GitHub ipynb](./code/bonus/softmax-regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/softmax-regression.ipynb)]
 
 <hr>
 

code/README.md

@@ -32,6 +32,8 @@ Simply click on the `ipynb`/`nbviewer` links next to the chapter headlines to vi
 - An Extended Nested Cross-Validation Example [[dir](./bonus)] [[ipynb](./bonus/nested_cross_validation.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/nested_cross_validation.ipynb)]
 - A Simple(r) Barebones Flask Webapp Template [[view directory](./bonus/flask_webapp_ex01)][[download as zip-file](https://github.com/rasbt/python-machine-learning-book/raw/master/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip)]
 - Reading handwritten digits from MNIST into NumPy arrays [[GitHub ipynb](./bonus/reading_mnist.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/reading_mnist.ipynb)]
+- Scikit-learn Model Persistence using JSON [[GitHub ipynb](./bonus/scikit-model-to-json.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/scikit-model-to-json.ipynb)]
+- Multinomial logistic regression / softmax regression [[GitHub ipynb](./bonus/softmax-regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/softmax-regression.ipynb)]
 
 
 

code/bonus/README.md

@@ -16,3 +16,5 @@ A collection of additional notebooks and code examples to clarify and explain co
 - Reading handwritten digits from MNIST into NumPy arrays [[GitHub ipynb](./reading_mnist.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/reading_mnist.ipynb)]
 
 - Scikit-learn Model Persistence using JSON [[GitHub ipynb](./scikit-model-to-json.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/scikit-model-to-json.ipynb)]
+
+- Multinomial logistic regression / softmax regression [[GitHub ipynb](./softmax-regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/softmax-regression.ipynb)]

code/bonus/scikit-model-to-json.ipynb

@@ -59,7 +59,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Scikit-learn Model Persistence using JSON"
+    "# Bonus Material - Scikit-learn Model Persistence using JSON"
    ]
   },
   {