In the second part of a series of articles about the history of artificial intelligence, we look at important published papers in the history of AI and the concept introduced in them.
To train a model or use transfer learning in machine vision, there must be enough data. Data Augmentation is a very important step that helps us increase our training data. The purpose of this article is to examine this feature and to review the techniques used to increase data on the AIEX platform.
“Transfer learning” is a machine learning concept that involves storing machine knowledge and using it on different, related problems. For example, the knowledge gained from learning to recognize cars (from car images or videos) can also be applied to bus recognition problems.