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* Added ch16/readme

* Fixed the notebook links
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vmirly authored Dec 1, 2019
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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.

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