Skip to content

jnewmano/workshops

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

51 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

TensorFlow Workshops

Exercises for use at events.

How-to run these notebooks

To run these notebooks, you'll need to:

  1. Install TensorFlow
  2. Install Jupyter
  3. Clone this repo
  4. Start Jupyter, and open a notebook

Install TensorFlow by following these instructions.

Next, open a terminal and install additional dependencies used by these exercises. Note: if you installed TensorFlow using a virtual environment, be sure to activate the environment before running this command.

$ pip install -U numpy jupyter matplotlib pandas Pillow

Next, clone the workshops repo.

$ git clone https://github.com/tensorflow/workshops
$ cd workshops

Finally, start Jupyter.

$ jupyter notebook

You will see output on your terminal to indicate the server is running. Towards the end of the output, you will see a line similar to this.

Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
        http://localhost:8888/?token=e9fbab4702ac162eb1f1fc5...

Copy this URL, and paste it into your browser. Finally, navigate to the examples folder, and open the first notebook.

Slides

Here's a link to slides you can use for this workshop: https://goo.gl/bq8HAE Note: these are very basic at the moment. Improving them is on our roadmap.

Would you like to contribute, or report a bug?

Thanks! Can you please file an issue, or even better, a pull request? Future developers will appreciate your help. If it's your first time contributing to a TensorFlow project, there's just one legal hurdle to jump through first. Please review the contribution guidelines.


This is not an official Google product.

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 99.5%
  • Python 0.5%