add an overview readme

Author: rasbt

README.md

@@ -25,9 +25,11 @@ Kindle ASIN: B0742K7HYF
 
 ## Table of Contents and Code Notebooks
 
-**Helpful installation and setup instructions can be found in the README.md file [code/](code/) subdirectory**
+**Helpful installation and setup instructions can be found in the [README.md file of Chapter 1](code/ch01/README.md)**
 
-Simply click on the `open dir` links next to the chapter headlines to navigate to the chapter subdirectories located in the [code/](code/) subdirectory. You can also click on the `ipynb` links below to open and view the Jupyter notebook of each chapter directly on GitHub.
+To access the code materials for a given chapter, simply click on the `open dir` links next to the chapter headlines to navigate to the chapter subdirectories located in the [code/](code/) subdirectory. You can also click on the `ipynb` links below to open and view the Jupyter notebook of each chapter directly on GitHub.
+
+In addition, the [code/](code/) subdirectories also contain .py script files, which were created from the Jupyter Notebooks. However, I highly recommend working with the Jupyter notebook if possible in your computing environment. Not only do the Jupyter notebooks contain the images and section headings for easier navigation, but they also allow for a stepwise execution of individual code snippets, which -- in my opinion -- provide a better learning experience.
 
 **Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.**   
 

code/README.md

@@ -4,42 +4,34 @@
 
 - A [quick NumPy introduction/refresher](https://sebastianraschka.com/pdf/books/dlb/appendix_f_numpy-intro.pdf) that covers everything (and more) you'd need for this book
 
-## Table of contents and code notebooks
-
-
-Simply click on the `ipynb`/`nbviewer` links next to the chapter headlines to view the code examples (currently, the internal document links are only supported by the NbViewer version).
-**Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.**   
+## Table of Contents and Code Notebooks
 
+**Helpful installation and setup instructions can be found in the [README.md file of Chapter 1](ch01/README.md)**
 
+To access the code materials for a given chapter, simply click on the `open dir` links next to the chapter headlines to navigate to the chapter subdirectories located in the [code/](code/) subdirectory. You can also click on the `ipynb` links below to open and view the Jupyter notebook of each chapter directly on GitHub.
 
-<br>
+In addition, the [code/](code/) subdirectories also contain .py script files, which were created from the Jupyter Notebooks. However, I highly recommend working with the Jupyter notebook if possible in your computing environment. Not only do the Jupyter notebooks contain the images and section headings for easier navigation, but they also allow for a stepwise execution of individual code snippets, which -- in my opinion -- provide a better learning experience.
 
+**Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.**   
 
-1. Machine Learning - Giving Computers the Ability to Learn from Data [[dir](./ch01)] [[ipynb](./ch01/ch01.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb)]
-2. Training Machine Learning Algorithms for Classification [[dir](./ch02)] [[ipynb](./ch02/ch02.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch02/ch02.ipynb)]
-3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[dir](./ch03)] [[ipynb](./ch03/ch03.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch03/ch03.ipynb)]
-4. Building Good Training Sets – Data Pre-Processing [[dir](./ch04)] [[ipynb](./ch04/ch04.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch04/ch04.ipynb)]
-5. Compressing Data via Dimensionality Reduction [[dir](./ch05)] [[ipynb](./ch05/ch05.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch05/ch05.ipynb)]
-6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[dir](./ch06)] [[ipynb](./ch06/ch06.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb)]
-7. Combining Different Models for Ensemble Learning [[dir](./ch07)] [[ipynb](./ch07/ch07.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch07/ch07.ipynb)]
-8. Applying Machine Learning to Sentiment Analysis [[dir](./ch08)] [[ipynb](./ch08/ch08.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch08/ch08.ipynb)]
-9. Embedding a Machine Learning Model into a Web Application [[dir](./ch09)] [[ipynb](./ch09/ch09.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch09/ch09.ipynb)]
-10. Predicting Continuous Target Variables with Regression Analysis [[dir](./ch10)] [[ipynb](./ch10/ch10.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch10/ch10.ipynb)]
-11. Working with Unlabeled Data – Clustering Analysis [[dir](./ch11)] [[ipynb](./ch11/ch11.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch11/ch11.ipynb)]
-12. Training Artificial Neural Networks for Image Recognition [[dir](./ch12)] [[ipynb](./ch12/ch12.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb)]
-13. Parallelizing Neural Network Training via Theano [[dir](./ch13)] [[ipynb](./ch13/ch13.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch13/ch13.ipynb)]
-
-<br>
 
-**Bonus Notebooks (not in the book)**
+1. Machine Learning - Giving Computers the Ability to Learn from Data [[open dir](./ch01)] [[ipynb](./ch01/ch01.ipynb)] 
+2. Training Machine Learning Algorithms for Classification [[open dir](./ch02)] [[ipynb](./ch02/ch02.ipynb)] 
+3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[open dir](./ch03)] [[ipynb](./ch03/ch03.ipynb)] 
+4. Building Good Training Sets – Data Pre-Processing [[open dir](./ch04)] [[ipynb](./ch04/ch04.ipynb)] 
+5. Compressing Data via Dimensionality Reduction [[open dir](./ch05)] [[ipynb](./ch05/ch05.ipynb)] 
+6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[open dir](./ch06)] [[ipynb](./ch06/ch06.ipynb)]
+7. Combining Different Models for Ensemble Learning [[open dir](./ch07)] [[ipynb](./ch07/ch07.ipynb)]
+8. Applying Machine Learning to Sentiment Analysis [[open dir](./ch08)] [[ipynb](./ch08/ch08.ipynb)] 
+9. Embedding a Machine Learning Model into a Web Application [[open dir](./ch09)] [[ipynb](./ch09/ch09.ipynb)] 
+10. Predicting Continuous Target Variables with Regression Analysis [[open dir](./ch10)] [[ipynb](./ch10/ch10.ipynb)] 
+11. Working with Unlabeled Data – Clustering Analysis [[open dir](./ch11)] [[ipynb](./ch11/ch11.ipynb)] 
+12. Implementing a Multi-layer Artificial Neural Network from Scratch [[open dir](./ch12)] [[ipynb](./ch12/ch12.ipynb)] 
+13. Parallelizing Neural Network Training with TensorFlow [[open dir](./ch13)] [[ipynb](./ch13/ch13.ipynb)] 
+14. Going Deeper: The Mechanics of TensorFlow [[open dir](./ch14)] [[ipynb](./ch14/ch14.ipynb)] 
+15. Classifying Images with Deep Convolutional Neural Networks [[open dir](./ch15)] [[ipynb](./ch15/ch15.ipynb)] 
+16. Modeling Sequential Data Using Recurrent Neural Networks [[open dir](./ch16)] [[ipynb](./ch16/ch16.ipynb)] 
 
-- Logistic Regression Implementation [[dir](./bonus)] [[ipynb](./bonus/logistic_regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/logistic_regression.ipynb)]
-- A Basic Pipeline and Grid Search Setup [[dir](./bonus)] [[ipynb](./bonus/svm_iris_pipeline_and_gridsearch.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)]
-- 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)]