Generalizing the Model
In the next video segment, you would note that I am holding up a significantly smaller ball for the model to test. At this stage, the model incorrectly classifies the small ball as either a wallet, or as the background. As such the model is unable to generalize. This means that when I test the model with data that we are expected to see during production, it will probably fail.
1. Microsoft Azure Machine Learning x Udacity — Lesson 4 Notes
2. Fundamentals of AI, ML and Deep Learning for Product Managers
3. Roadmap to Data Science
4. Work on Artificial Intelligence Projects
In fact the generalization of the model is probably one of the most important steps you would need to carry out. This ensures that the data used to train the model is representative of the data that the model will be seeing after it is deployed. In the video segment below, you would notice that I added photos of me holding a smaller ball which is sent to be trained in the model.
Thereafter the model that was trained was able to identify balls of different shapes, colors and sizes.