Skip to content

Latest commit

 

History

History
63 lines (42 loc) · 3.07 KB

README.md

File metadata and controls

63 lines (42 loc) · 3.07 KB
Raw

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.

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.

(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 and 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.

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.