Added ch14/readme (rasbt#93)

* Added ch14/readme

* Fixed the notebook links

Author: vmirly

ch13/README.md

@@ -56,7 +56,7 @@ A window will open up in your browser, which you can then use to navigate to the
 
 **More installation and setup instructions can be found in the [README.md file of Chapter 1](../ch01/README.md)**.
 
-**(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: [`ch12.ipynb`](ch12.ipynb))**
+**(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: [`ch14_part1.ipynb`](ch14_part1.ipynb) and [`ch14_part2.ipynb`](ch14_part2.ipynb))**
 
 In addition to the code examples, I added a table of contents to each Jupyter notebook as well as section headers that are consistent with the content of the book. Also, I included the original images and figures in hope that these make it easier to navigate and work with the code interactively as you are reading the book.
 

ch14/README.md

@@ -0,0 +1,61 @@
+Python Machine Learning - Code Examples
+
+
+##  Chapter 14: Going Deeper – The Mechanics of TensorFlow
+
+
+### Chapter Outline
+
+- The key features of TensorFlow
+  - TensorFlow's computation graphs: migrating to TensorFlow v2
+     - Understanding computation graphs
+     - Creating a graph in TensorFlow v1.x
+     - Migrating a graph to TensorFlow v2
+     - Loading input data into a model: TensorFlow v1.x style
+     - Loading input data into a model: TensorFlow v2 style
+  - Improving computational performance with function decorators
+  - TensorFlow Variable objects for storing and updating model parameters
+  - Computing gradients via automatic differentiation and GradientTape
+     - Computing the gradients of the loss with respect to trainable variables
+     - Computing gradients with respect to nontrainable tensors
+     - Keeping resources for multiple gradient computations
+- Simplifying implementations of common architectures via the Keras API
+  - Solving an XOR classification problem
+  - Making model building more flexible with Keras' functional API
+  - Implementing models based on Keras' Model class
+  - Writing custom Keras layers
+- TensorFlow Estimators
+  - Working with feature columns
+  - Machine learning with pre-made Estimators
+  - Using Estimators for MNIST handwritten digit classification
+  - Creating a custom Estimator from an existing Keras model
+- Summary
+
+### A note on using the code examples
+
+The recommended way to interact with the code examples in this book is via Jupyter Notebook (the `.ipynb` files). Using Jupyter Notebook, you will be able to execute the code step by step and have all the resulting outputs (including plots and images) all in one convenient document.
+
+![](../ch02/images/jupyter-example-1.png)
+
+
+
+Setting up Jupyter Notebook is really easy: if you are using the Anaconda Python distribution, all you need to install jupyter notebook is to execute the following command in your terminal:
+
+    conda install jupyter notebook
+
+Then you can launch jupyter notebook by executing
+
+    jupyter notebook
+
+A window will open up in your browser, which you can then use to navigate to the target directory that contains the `.ipynb` file you wish to open.
+
+**More installation and setup instructions can be found in the [README.md file of Chapter 1](../ch01/README.md)**.
+
+**(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: [`ch12.ipynb`](ch12.ipynb))**
+
+In addition to the code examples, I added a table of contents to each Jupyter notebook as well as section headers that are consistent with the content of the book. Also, I included the original images and figures in hope that these make it easier to navigate and work with the code interactively as you are reading the book.
+
+![](../ch02/images/jupyter-example-2.png)
+
+
+When I was creating these notebooks, I was hoping to make your reading (and coding) experience as convenient as possible! However, if you don't wish to use Jupyter Notebooks, I also converted these notebooks to regular Python script files (`.py` files) that can be viewed and edited in any plaintext editor.