Name: Deep Dive into TensorFlow
Start: 2016-06-29T18:30:00-04:00
End: 2016-06-29T21:00:00-04:00
Location: Sapient

Please confirm your attendance! Due to security concerns we need the First and Last name - as on your government issued ID registered using EVENTBRITE.COM (https://www.eventbrite.com/e/washington-dc-meetup-deep-dive-into-tensorflow-tickets-26035651334)

Many thanks to Sapient Glabal Markets (http://www.sapientglobalmarkets.com) for hosting and sponsoring Tensorflow meetup.

Agenda:

6:30 - Doors open. Networking. Members meet each other. Pizza and beer

7:00 - TensorFlow: A Calculus of Variations for Data Science by Dr. Kirk Borne, Principal Data Scientist @ Booz Allen Hamilton

7:20 - Q&A break.

7:30 - Hello, TensorFlow! by Aaron Schumacher, Deep Learning Analytics

8:20 - Q&A break

8:40 - Wrap-up.

DETAILED AGENDA:

TensorFlow: A Calculus of Variations for Data Science

https://raw.githubusercontent.com/veronicaryan8/HL/master/Washington%20D.C._June%2029th_Presentation_Tensorflow%20%26%20OpenAI%20(1).jpg

This talk will focus more on what and why, not so much on how. In this talk, Deep Dive refers to a novice being thrown into the deep end of the pool. As a novice in any area, I begin to learn through analogies, first principles, basic assumptions, key motivations, and why I should care. Experts can show you how to do TensorFlow (and that's their gift), but I want to show you what is going on under the hood and why it makes sense to do TensorFlow (and that's my goal).

Dr. Kirk Borne is the Principal Data Scientist at Booz Allen Hamilton (since 2015). He previously spent 12 years as Professor of Astrophysics and Computational Science at George Mason University where he taught and advised students in the graduate and undergraduate Data Science degree programs. Before that, he worked 18 years supporting NASA projects in various roles, including program manager in the Space Science Data Operations Office and Data Archive Project Scientist for the Hubble Space Telescope. He has a PhD in Astronomy from Caltech and a BS in Physics from LSU. He is an active contributor on social media, where he is an advocate of data literacy for all and has been named consistently among the top worldwide influencers in big data and data science since 2013. Follow him on Twitter at @KirkDBorne.

Hello, TensorFlow!

https://raw.githubusercontent.com/veronicaryan8/HL/master/Washington%20D.C._June%2029th_Presentation_Tensorflow%20%26%20OpenAI.jpg

How does TensorFlow work? Let's break it down so that we can see and understand every moving part. We'll explore the data flow graph (https://en.wikipedia.org/wiki/Graph_(abstract_data_type)) that defines the computations your data will undergo; how to train models with gradient descent (https://en.wikipedia.org/wiki/Gradient_descent) using TensorFlow; and how TensorBoard (https://www.tensorflow.org/versions/r0.8/how_tos/summaries_and_tensorboard/) can visualize your TensorFlow work. The examples here won't solve industrial machine learning problems, but they'll help you understand the components underlying everything built with TensorFlow, including whatever you build next!

Aaron Schumacher is a data scientist and software engineer for Deep Learning Analytics. He has taught with Python and R for General Assembly and the Metis data science bootcamp. Aaron has also worked with data at Booz Allen Hamilton, New York University, and the New York City Department of Education. He studied mathematics at the University of Wisconsin–Madison and teaching mathematics at Bard College. Aaron's career-best breakdancing result was advancing to the semi-finals of the R16 Korea 2009 individual footwork battle. He is honored to now be the least significant contributor to TensorFlow 0.9 (https://github.com/tensorflow/tensorflow/releases/tag/v0.9.0rc0).

Please confirm your attendance! Due to security concerns we need the First and Last name - as on your government issued ID registered using EVENTBRITE.COM (https://www.eventbrite.com/e/washington-dc-meetup-deep-dive-into-tensorflow-tickets-26035651334)

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Deep Dive into TensorFlow

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