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

Tensor abuse in the workplace

Ted Dunning (MapR Technologies)
2:05pm2:45pm Thursday, September 28, 2017
Data science & advanced analytics, Machine Learning & Data Science
Location: 1A 06/07 Level: Intermediate
Average rating: ****.
(4.50, 2 ratings)

Who is this presentation for?

  • Data scientists, data engineers, data analysts, and software engineers

What you'll learn

  • Understand why tensors are so important in machine learning
  • Explore the internals of TensorFlow and related systems such as Theano
  • Learn emerging techniques for high-performance numerical computing, particularly but not exclusively in the area of machine learning


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 Technologies

Ted Dunning is chief applications architect at MapR Technologies. He’s also a board member for the Apache Software Foundation, a PMC member and committer of the Apache Mahout, Apache Zookeeper, and Apache Drill projects, and a mentor for various incubator projects. Ted contributed to clustering, classification, and matrix decomposition algorithms in Mahout and to the new Mahout Math library. He also 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. He holds a PhD in computing science from the University of Sheffield. He is on Twitter as @ted_dunning.