In this chapter, the Convolution Neural Network (CNN) architecture built in Chapter 9, Getting Your Neurons to Work, was loaded to classify physical gaps in a food processing company.
Then the trained models were applied to transfer learning by identifying similar types of images. Some of these images represented concepts that lead the trained CNN to identify Γ concept gaps.
Γ concept gaps were applied to different fields using the CNN as a training and classification tool in domain learning.
Γ concept gaps have two main properties: negative n-gaps and positive p-gaps. To distinguish one from the other, a CRLMM provides a useful add-on. In the food processing company, installing a webcam on the right food processing conveyor belt provided a context for the system to decide whether the gap was positive or negative.
With these concepts in mind, let&apos...
