Presented By O'Reilly and Cloudera
Make Data Work
September 26–27, 2016: Training
September 27–29, 2016: Tutorials & Conference
New York, NY

Deep learning with TensorFlow

Martin Wicke (Google), joshua gordon (Google)
1:30pm–5:00pm Tuesday, 09/27/2016
Data science & advanced analytics
Location: 1 E 07/1 E 08 Level: Intermediate
Average rating: ***..
(3.47, 15 ratings)

Prerequisite knowledge

  • A general understanding of Python
  • Materials or downloads needed in advance

  • A laptop with TensorFlow installed (If you run into trouble configuring TensorFlow's dependencies, try installing Docker.)
  • What you'll learn

  • Gain a hands-on introduction to TensorFlow and deep learning
  • Learn how to build and deploy simple and complex models with TensorFlow
  • Description

    Martin Wicke and Josh Gordon offer hands-on experience training and deploying a machine-learning system using TensorFlow, a popular open source library. You’ll learn how to build machine-learning systems from simple classifiers to complex image-based models as well as how to deploy models in production using TensorFlow Serving, a high-performance open source system designed for serving machine-learned models.

    Photo of Martin Wicke

    Martin Wicke

    Google

    Martin Wicke is a software engineer working on making sure that TensorFlow is a thriving open source project. Before joining Google’s Brain team, Martin worked in a number of startups and did research on computer graphics at Berkeley and Stanford.

    Photo of joshua gordon

    joshua gordon

    Google

    Josh Gordon is a developer advocate for TensorFlow at Google. He’s passionate about machine learning and computer science education. In his free time, Josh loves biking, running, and exploring the great outdoors.

    Comments on this page are now closed.

    Comments

    Picture of Mike Lee Williams
    Mike Lee Williams
    09/30/2016 12:26pm EDT

    @KrishnaKanth Erodula https://github.com/random-forests/tensorflow-workshop

    KrishnaKanth Erodula
    09/27/2016 7:57am EDT

    where can i find the source code for this tutorial