Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

In-Person Training
Machine learning from scratch in TensorFlow

Dylan Bargteil (The Data Incubator)
9:00am–5:00pm Tuesday, 09/11/2018
Location: 1A 03
Secondary topics:  Deep Learning

To attend a training course, you must be registered for a Platinum or Training pass; does not include access to tutorials on Tuesday.

The TensorFlow library provides for the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. Dylan Bargteil introduces TensorFlow's capabilities through its Python interface.

What you'll learn, and how you can apply it

  • Understand TensorFlow's capabilities through its Python interface

Prerequisites:

  • Familiarity with Python, matrices, modeling, and statistics
  • No experience with TensorFlow required

Hardware and/or installation requirements:

  • A laptop (You'll be provisioned a cloud instance of TensorFlow.)

The TensorFlow library provides for the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms.

Dylan Bargteil introduces TensorFlow’s capabilities through its Python interface. Starting at the low level of the TensorFlow graph, you’ll build fundamental machine learning tools like linear regression and softmax classification before combining them into a basic neural network. You’ll leave with an appreciation of the low-level operations underlying all neural networks.

Schedule:

  • Introduction to TensorFlow
  • Iterative algorithms
  • Machine learning
  • Basic neural networks

About your instructor

Photo of Dylan Bargteil

Dylan Bargteil is a data scientist in residence at the Data Incubator, where he works on research-guided curriculum development and instruction. Previously, he worked with deep learning models to assist surgical robots and was a research and teaching assistant at the University of Maryland, where he developed a new introductory physics curriculum and pedagogy in partnership with HHMI. Dylan studied physics and math at University of Maryland and holds a PhD in physics from New York University.

Twitter for thedatainc

Conference registration

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Comments

Brinda Mo | DATA SCIENTIST
09/21/2018 7:54am EDT

Hi. I am wondering if I can share this material with the rest of the organization?