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Python Machine Learning - Code Examples | ||
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## Chapter 16: Modeling Sequential Data Using Recurrent Neural Networks | ||
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### Chapter Outline | ||
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- 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 | ||
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### A note on using the code examples | ||
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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. | ||
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![](../ch02/images/jupyter-example-1.png) | ||
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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: | ||
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conda install jupyter notebook | ||
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Then you can launch jupyter notebook by executing | ||
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jupyter notebook | ||
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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. | ||
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**More installation and setup instructions can be found in the [README.md file of Chapter 1](../ch01/README.md)**. | ||
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**(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))** | ||
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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. | ||
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![](../ch02/images/jupyter-example-2.png) | ||
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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. |