Colfax Training on Machine Learning at the University of Washington

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Friday, October 6, 2017
9:30 am - 4:00 pm

University of Washington, eScience Institute
3910 15th Ave NE, Seattle, WA, 98195, Physics/Astronomy Tower, 6th floor (WRF Data Science Studio)

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Machine Learning and High-Performance Computing: Intel Architecture and Artificial Intelligence

Intel and Colfax International are offering a brand-new hands-on training on machine learning application in a high-performance computing setting. This training delivers practical knowledge on accelerating machine learning pipelines by extracting parallel compute performance potential found in both Intel® Xeon® and Intel® Xeon Phi™ processors. The course materials and practical exercises are appropriate for machine learning developers of any experience level.

The training is conducted by Colfax International.
Registration is open to everyone free of charge thanks to Intel’s sponsorship.


Hands-On Component

Colfax Developer Training is far more than a lecture – it is an experiential learning program. That is because the training contains hands-on component in two forms:

  1. The instructor will demonstrate the methods taught in the course live, on servers with the latest Intel Xeon Phi processors.
  2. Attendees will receive remote access to these training servers for 1 day and a set of programming and optimization exercises.

Bring your own laptop to take advantage of this opportunity. The exercises are written in Python and are presented through Jupyter notebook.


 Colfax_Machine_Learning_Training.pdf (6 MB) — this file is available only to registered users. Register or Log In.

Examples: (7 KB) — this file is available only to registered users. Register or Log In.


xeon_k_ww_rgb_300 xeonphi_k_ww_rgb_300 intel-software-logo-small

  • Registration, light breakfast (9:30 – 10:00 am)
  • Morning session (10:00 am – 1:00 pm)
    • Intel Architecture and AI (15 min)
    • Using the Remote Access Resource (15 min)
    • Optimized Libraries (30 min)
    • Maximizing Computational Performance of Python (1.5 hours)
      • Hands-on Demo: Data Exploration and Data structures
      • Hands-on Demo: Transformations and “Vectorization”
      • Hands-on Demo: Learning on Multi-core Systems
  • Distributed Computing (30 min)
    • MapReduce-like workloads and Intel DAAL
  • Lunch (1:00 pm – 1:30 pm)
  • Afternoon session (1:30 pm – 4:00 pm)
    • Neural Networks and Tensorflow (1.5 hours)
      • Hands-on Demo: Basics and Getting on the Same Page
      • Hands-on Demo: Convolutional Neural Networks
      • Hands-on Demo: RNN and NLP
    • Q&A, Other Frameworks, Extra Time for Examples(1 hour)


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