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 at Google working on making sure that TensorFlow is a thriving open source project. Previously, 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 at Google AI and teaches applied deep learning at Columbia University and machine learning at Pace University. He has over a decade of machine learning experience to share. You can find him on Twitter as @random_forests.

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    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