Optimizing Machine Learning with TensorFlow, ActivePython and Intel

Webinar: Optimizing Machine Learning with TensorFlow, ActivePython and Intel

Optimizing Machine Learning with TensorFlow, ActivePython and Intel

Tensorflow, developed by Google, has become the most popular framework for deep learning, and now operates on a variety of devices such as multicore CPUs, general purpose GPUs, mobile devices, and custom ASICs.

In this webinar co-hosted by Intel, you will get a general introduction to working with Tensorflow and its surrounding ecosystem, general problem classes, where you can get big acceleration, and why run on a CPU.

Topics covered:

  • Ideal use cases for TensorFlow on CPUs, including which models and types of operations benefit the most
  • Proposed benchmarks, projected accelerations, and how to tune performance for your systems
  • Advanced topics like using multiple nodes to train on large data sets
  • How Intel has optimized TensorFlow for Intel CPUs by fully utilizing multi-core processors, AVX instructions, and high performance memory systems
  • How other Intel MKL-optimized data science packages included with ActivePython can help accelerate your algorithms

Download the slides (PDF) here.

Time to watch: 1 hr 2 min

Speakers:
Mohammad Ashraf Bhuiyan, Intel Artificial Intelligence Group, Senior Software Engineer
Pete Garcin, Developer Advocate, ActiveState

Watch the Webinar



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