Merge branch 'master' of github.com:rasbt/python-machine-learning-book-4rd-edition into ch12-readme

Author: vmirly

ch13/README.md

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+Python Machine Learning - Code Examples
+
+
+##  Chapter 13: Parallelizing Neural Network Training with TensorFlow 
+
+
+### Chapter Outline
+
+- TensorFlow and training performance
+  - Performance challenges
+  - What is TensorFlow?
+  - How we will learn TensorFlow
+- First steps with TensorFlow
+  - Installing TensorFlow
+  - Creating tensors in TensorFlow
+  - Manipulating the data type and shape of a tensor
+  - Applying mathematical operations to tensors
+  - Split, stack, and concatenate tensors
+  - Building input pipelines using tf.data – the TensorFlow Dataset API
+    - Creating a TensorFlow Dataset from existing tensors
+    - Combining two tensors into a joint dataset
+    - Shuffle, batch, and repeat
+    - Creating a dataset from files on your local storage disk
+    - Fetching available datasets from the `tensorflow_datasets` library
+- Building an NN model in TensorFlow
+  - The TensorFlow Keras API (tf.keras)
+  - Building a linear regression model
+  - Model training via the `.compile()` and `.fit()` methods 
+  - Building a multilayer perceptron for classifying flowers in the Iris dataset
+  - Evaluating the trained model on the test dataset
+  - Saving and reloading the trained model
+- Choosing activation functions for multilayer NNs
+  - Logistic function recap
+  - Estimating class probabilities in multiclass classification via the softmax function
+  - Broadening the output spectrum using a hyperbolic tangent
+  - Rectified linear unit activation
+- 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.