From 0689fe6405f7d7b5bd9b6fe0ca3a648685b76a44 Mon Sep 17 00:00:00 2001
From: Vahid Mirjalili <2880929+vmirly@users.noreply.github.com>
Date: Sun, 1 Dec 2019 11:15:14 -0600
Subject: [PATCH] Added ch16/readme (#94)

* Added ch16/readme

* Fixed the notebook links
---
 ch16/README.md | 63 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 63 insertions(+)
 create mode 100644 ch16/README.md

diff --git a/ch16/README.md b/ch16/README.md
new file mode 100644
index 00000000..1cf12b76
--- /dev/null
+++ b/ch16/README.md
@@ -0,0 +1,63 @@
+Python Machine Learning - Code Examples
+
+
+##  Chapter 16: Modeling Sequential Data Using Recurrent Neural Networks
+
+
+### Chapter Outline
+
+- Introducing sequential data
+  - Modeling sequential data—order matters
+  - Representing sequences
+  - The different categories of sequence modeling
+- RNNs for modeling sequences
+  - Understanding the RNN looping mechanism
+  - Computing activations in an RNN
+  - Hidden-recurrence versus output-recurrence
+  - The challenges of learning long-range interactions
+  - Long short-term memory cells
+- Implementing RNNs for sequence modeling in TensorFlow
+  - Project one: predicting the sentiment of IMDb movie reviews
+    - Preparing the movie review data
+    - Embedding layers for sentence encoding
+    - Building an RNN model
+    - Building an RNN model for the sentiment analysis task
+  - Project two: character-level language modeling in TensorFlow
+    - Preprocessing the dataset
+    - Building a character-level RNN model
+    - Evaluation phase: generating new text passages
+- Understanding language with the Transformer model
+  - Understanding the self-attention mechanism
+  - A basic version of self-attention
+  - Parameterizing the self-attention mechanism with query, key, and value weights
+  - Multi-head attention and the Transformer block
+- 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: [`ch16_part1.ipynb`](ch16_part1.ipynb) and [`ch16_part2.ipynb`](ch16_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.
+
+![](../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.