Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

Getting started with TensorFlow

Yufeng Guo (Google), Amy Unruh (Google)
9:00am12:30pm Tuesday, September 26, 2017
Data science & advanced analytics, Machine Learning & Data Science
Location: 1A 21/22 Level: Intermediate
Average rating: **...
(2.00, 9 ratings)

Who is this presentation for?

  • Developers interested in machine learning

Prerequisite knowledge

  • A basic understanding of Python
  • Familiarity with machine learning (useful but not required)

Materials or downloads needed in advance

What you'll learn

  • Learn how to build and deploy simple and complex models with TensorFlow

Description

Yufeng Guo and Amy Unruh walk you through training and deploying a machine learning system using TensorFlow, a popular open source library. Yufeng and Amy take you from a conceptual overview all the way to building complex classifiers and explain how you can apply deep learning to complex problems in science and industry.

Photo of Yufeng Guo

Yufeng Guo

Google

Yufeng Guo is a developer advocate for the Google Cloud Platform, where he is trying to make machine learning more understandable and usable for all. He enjoys hearing about new and interesting applications of machine learning, so be sure to share your use case with him on Twitter.

Photo of Amy Unruh

Amy Unruh

Google

Amy Unruh is a developer programs engineer for the Google Cloud Platform, where she focuses on machine learning and data analytics as well as other Cloud Platform technologies. Amy has an academic background in CS/AI and has also worked at several startups, done industrial R&D, and published a book on App Engine.

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Comments

Picture of Amy Unruh
Amy Unruh | DEVELOPER RELATIONS
09/26/2017 11:16am EDT

Marcin,
Thanks, that’s useful feedback.
On deployment, if you’re interested in deploying on Cloud ML Engine (https://cloud.google.com/ml-engine/), the last part of the workshop had some info on doing that (that we did not have time to cover):
https://github.com/amygdala/tensorflow-workshop/tree/master/workshop_sections/mnist_series/mnist_cnn_custom_estimator#training-on-cmle—using-fashion-mnist

(We also will have an example coming soon, on doing distributed training on GKE. I’ll try to remember to update here when it’s out.)

Marcin Tustin |
09/26/2017 7:38am EDT

I know how hard it is to pitch workshop sessions just right for the audience, and it doesn’t help if the conference organizers write the description. Maybe in future this could include a fairly detailed description of the curriculum for the session?

I was hoping for a deep intro into deployment and execution, optimization, and the like. We had one attendee who asked what differentiation is (presumably relevance, rather than the concept itself).

Picture of Yufeng Guo
Yufeng Guo | DEVELOPER ADVOCATE
09/22/2017 3:20pm EDT

No need to have Docker for this :)

Ganesh Prasad | DATA SCIENTIST
09/22/2017 10:26am EDT

Hi,

Most of the Tensorflow tutorials i have seen use Docker but Docker does not go through our company laptops behind the firewall. Do you know if that is a requirement for this tutorial?