Code, Slides and other material for the talk on Deep Learning using TensorFlow at Datagiri meetup (Feb, 2017)

1. Install Anaconda
2. Install necessary packages

Notes

• In the following steps, we install a cpu-specific version of tensorflow, which is good enough for the session, but not for most real-world tasks.
• The instructions have been tested only on Ubuntu and OS X (we didn't have a windows system available for testing; please raise a issue if you hit any snags on your windows system).

(Updated from Udacity's instructions)

Per the Anaconda docs:

Conda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them. It works on Linux, OS X and Windows, and was created for Python programs but can package and distribute any software.

Using Anaconda consists of the following:

1. Install miniconda on your computer
2. Create a new conda environment
3. Each time you wish to work, activate your conda environment

Download the version of miniconda that matches your system. Make sure you download the version for Python 3.x (3.6 is the latest at the time of writing).

Install miniconda on your machine. Detailed instructions:

• Linux: http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install
• Mac: http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install
• Windows: http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install

Setup your the tensorflow environment.

git clone https://github.com/soumendra/lecture_datagiri_deeplearning_tensorflow_Feb17.git
cd lecture_datagiri_deeplearning_tensorflow_Feb17

If you are on Windows, rename
meta_windows_patch.yml to
meta.yml

Create tensorflow. Running this command will create a new conda environment that is provisioned with all libraries you need to run the notebooks.

conda env create -f environment.yml

Verify that the tensorflow environment was created in your environments:

conda info --envs

Cleanup downloaded libraries (remove tarballs, zip files, etc):

conda clean -tp

To uninstall the environment:

conda env remove -n tensorflow

Now that you have created an environment, in order to use it, you will need to activate the environment. This must be done each time you begin a new working session, i.e., open a new terminal window.

Activate the tensorflow environment:

$ source activate tensorflow

Depending on shell either:

$ source activate tensorflow

or

$ activate tensorflow

That's it. Now all of the tensorflow libraries are available to you. You can start a Jupyter Notebook with:

jupyter notebook

To exit the environment when you have completed your work session, simply close the terminal window.