Here is a link to the talk: https://www.youtube.com/watch?v=bvZnphPgz74
My tutorial for PyData Carolinas 2016. I start out with the basics of Theano and Keras, then dive a bit deeper with word embeddings and recurrent networks, and finish by talking about and visualizing how one type of attention works for RNNs.
Short URL to this page: goo.gl/pem8Pf
This set of activities give an introduction to deep language modeling, based on this blog post. It is ideal for people who are semi-comfortable using Theano or Tensorflow and have some familiarity with Keras, although if you've never tried any of them, the best way to learn is by doing!
The install.sh is a script I wrote to easily get it set up on my Mac, but it should work on a Linux system as well. The basic dependencies are Jupyter, Theano, Keras and matplotlib.
Install the Python package manager (if you don't already have it)
sudo easy_install pip # Mac
sudo apt install python-pip # Ubuntu
??? # WindowsInstall the virtual environment tool for managing dependencies (if you don't already have it)
sudo pip install virtualenvClone the repository somewhere on your computer and create a virtual environment to install the project dependencies
cd /path/to/desired/directory/
git clone https://github.com/codekansas/pydata-carolinas-2016
cd pydata-carolinas-2016
virtualenv venv
source venv/bin/activate
pip install --upgrade Theano keras h5py jupyter matplotlibYou can see the notebooks by launching the Jupyter workspace
ipython notebook- Theano XOR: The "Hello, world!" program. Implement a feed-forward network which learns an XOR function.
- Keras XOR: Showing that it is a lot easier and faster to use Keras.
- Word Embeddings: Understanding how word embeddings can help.
- Recurrent Networks: Building recurrent networks in Theano, and showing how much easier it is in Keras.
- Attentional RNNs: Modifying Keras to do special things.
If you want to contribute something, feel free! Just make sure it is grammatically correct and all the code works.