Learning Machines Fall 2016 — Week 7

Taught by Patrick Hebron at ITP, Fall 2016

Looking Ahead

In the second half of the semester, we will continue to investigate more elaborate machine learning algorithms while also starting to use a wider range of tools. These two elements as well as in-class workshopping and discussions about machine learning applications will help us to work towards final projects.

In class today, we will:

Get a basic understanding of what Docker and LaunchBot do.
Take a brief tour of the Jupyter notebook format.
Discuss the programming model used in TensorFlow.
Work through a few preliminary TensorFlow tutorials (including a word2vec example).
Discuss embedding spaces in Self-Organizing Maps, t-SNE and word2vec.

Hello, TensorFlow

This week, we will start to explore TensorFlow, using Docker and LaunchBot to streamline our workflow.

If you haven't done so already, please:

Sign-up for a LaunchBot account.
Follow the steps listed in the LaunchBot Getting Started document.

Once LaunchBot is running, in the "Clone an external project..." panel, enter the url for our class LaunchBot repo:

https://github.com/Hebali/launchbot-learning-machines.git

In LaunchBot's "Projects" tab, you should now see the launchbot-learning-machines project listed. Select this project and hit "Launch."

The first time you launch a project, it may take awhile to download and build the image.

Homework

Assignment:

Spend some time exploring the TensorFlow tutorials in our class LaunchBot and from the other sites linked there. Particularly, Aymeric Damien's TensorFlow-Examples, which includes many examples already formatted for Jupyter notebook.

Research Starting Points:

Readings:

For this week's reading, spend some time exploring the sites linked above.