AIDC Summit Series Registration - Intel AI

Join us for a day of hands-on AI learning

The Intel® AIDC Summit Series offers full-day training for small groups of enterprise developers who want to learn how to assemble the ideal hardware and software combinations for deep learning applications. These complimentary events are invitation only, and space is limited.

Learning Objectives:

  • Understand how to use Intel’s AI portfolio to solve deep learning problems
  • Preparing a dataset for model consumption – Preprocessing and data augmentation techniques
  • Decision metrics for choosing a framework
  • Decision metrics for choosing a network (topology)
  • Training a deep learning model using Tensorflow* with Intel optimizations
  • Deploying on the CPU, Integrated Graphics and Intel® Movidius™ Neural Compute Stick
Course Description
This course takes a hands-on approach to teaching the data science process using Intel’s AI portfolio of processors and optimized software. By using an enterprise image classification problem, the course explains the parameters to consider at each phase with the Jupyter* notebooks walking through the implementation. Learn the best-known methods for configuring hardware and software to run your application, including CPUs, integrated graphics, and the Intel® Movidius™ Neural Compute Stick. This course will cover:
 
  • Data set preparation
  • Framework selection
  • Network architecture
  • Software optimizations
  • Model training
  • Inference
Requirements and prerequisites:
 
 
Accessibility

If you have a disability-related accommodation, including dietary restrictions, auxiliary aids or services, to participate in an AIDC Summit, please contact us at least 5 business days prior to the scheduled event so that we make a reasonable effort to accommodate the request. For any additional questions, concerns, or feedback please contact us and our AIDC event team will respond within 48 hours of the request received.

Contact us: AIDCsummit@Intel.com

Sign up below for an event near you:

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