How to Validate a Deep Learning System in Manufacturing

Deep learning has become an indispensable technology in manufacturing, but a significant amount of work must be done up front to validate the system.

Although labor- and time-intensive, validation is critical to ensuring the success of your deep learning deployment. There are four primary phases involved in the process:

  1. Planning
  2. Data Collection and Ground Truth Labeling
  3. Optimization
  4. Factory Acceptance Testing

This white paper details each phase, providing the guidance you need to optimize deep learning in your manufacturing environment.

deep learning validation white paper flipbook image

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