An Introduction to TensorFlow

We are very pleased to have Jason Cherry stepping up to help us all understand TensorFlow a bit better. Jason will be covering:

• A brief intro on TensorFlow
• A comparison between TF and Theano, Keras & PyTorch.
• Going over how TensorFlow is used, first talking concept, and then doing a demo.
• Plans for the demo include how to use TF to build a few different machine learning models, including a traditional linear regression, and a standard feed-forward neural network.

Thanks for preparing this talk for us Jason.

Hope to see you all there!

--- Update 1/12/2017:
PyData Denver: Bob and I are thrilled to have so many RSVPs to next week's Introduction to Tensorflow meetup! Thank you for your enthusiasm -- at this time we have roughly 30% more RSVPs than for any previous PyData Denver meetup...and counting!

Due to the large number of RSVPs, Galvanize has moved our meetup from the basement to to the 4th floor atrium. We are still at Galvanize Platte.

Jason Cherry, our presenter, is "strongly encouraging" attendees to bring laptops to follow along. You don't have to do so, of course, but if you do wish to follow along, you have some pre-Meetup "homework." The Galvanize wifi can be difficult to get on, especially with a large group, so please do the following things before you arrive at the Meetup. We will not be able to help individuals get up to speed with downloading and configuring their systems during the talk:

Git clone Jason's repo: https://github.com/Calvinxc1/TensorFlow_Presentation. You will need to have the Jupyter notebook and test_data in that repo.
Install Tensorflow: https://www.tensorflow.org/install/
Install Python3.
Make sure you can open Jason's Jupyter notebook and at least run the first and second cell (to ensure TF is properly installed).
Also, to manage expectations, these are a few of Jason's notes for what the presentation will and will not be about:

What this is:

• An introduction on how to use TensorFlow.
• With specific examples on basic Machine Learning applications.

What this is not:

• An introduction to Machine Learning
• Python training
• An advanced TensorFlow course

Assumed Knowledge:

• Python 3.x
• Pandas
• Reading .csv data and converting to a NumPy array
• Numpy
• Using Pip
• Machine Learning Math:
• Linear Algebra
• Activation Functions (Linear vs. Sigmoid vs. ReLU vs. etc…) Finally, we plan to have a few adult beverages available at the Meetup (supplies limited, first come first serve), but will be unable to provide food. If you wish to eat before the Meetup, you might consider Brider, on the first floor of Galvanize (to your left as you enter the building). They have a Happy Hour from 3-6pm every day. Slices of pizza are only $2 during HH (and they are large slices).