Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA

Tensor abuse in the workplace

Ted Dunning (MapR, now part of HPE)
4:20pm5:00pm Wednesday, March 15, 2017
Data science & advanced analytics
Location: 230 C Level: Advanced
Secondary topics:  Hardcore Data Science
Average rating: ****.
(4.50, 6 ratings)

Who is this presentation for?

  • Data scientists, data engineers, and software engineers

Prerequisite knowledge

  • Basic software skills
  • Familiarity with numerical algorithms (useful but not required)

What you'll learn

  • Understand why tensors are so important in machine learning
  • Learn the basics of how to work with tensors


Tensors are the latest fad in machine learning, but there is real content beyond the buzzword. Tensors are the basic data type for modern numerical systems in much the same way that matrices were fundamental before. Tensors provide a consistent shorthand for describing a variety of computations in a way that is highly suitable for computation on GPUs, but they also provide a useful formalism for high-performance computation on ordinary processors. The reason that this works so well is that tensor operations not only allow the inner loop to be specified using numerical primitives but often also permit the enclosing two or three loops to be specified at the same time, enabling distributed computation with much less communication and thus much higher throughput.

Ted Dunning demystifies modern tensor-based computation systems by showing how they really just implement incredibly simple operations and allow us to express these operations very concisely. While tensor-based systems are often used for developing deep neural networks, Ted shows how they can be used for a number of other computations as well, sometimes in surprising ways—offering examples using TensorFlow that illustrate this simplicity and sophistication.

Photo of Ted Dunning

Ted Dunning

MapR, now part of HPE

Ted Dunning is the chief technology officer at MapR, an HPE company. He’s also a board member for the Apache Software Foundation, a PMC member, and committer on a number of projects. Ted has years of experience with machine learning and other big data solutions across a range of sectors. He’s contributed to clustering, classification, and matrix decomposition algorithms in Mahout and to the new Mahout Math library and designed the t-digest algorithm used in several open source projects and by a variety of companies. Previously, Ted was chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems and built fraud-detection systems for ID Analytics (LifeLock). Ted has coauthored a number of books on big data topics, including several published by O’Reilly related to machine learning, and has 24 issued patents to date plus a dozen pending. He holds a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting.