Develop an AI system to solve a real-world problem : Small Data and Cross Validation

The video tutorial addresses the challenge of limited data and how to use it efficiently by employing K-Fold cross validation. This method allows for more effective use of data by splitting it into multiple parts for testing and training, thus maximizing the data used for training. The tutorial explains how to implement this technique using a support vector machine model and the cross validation score command. It concludes with a discussion on the robustness of accuracy measures obtained through this method and introduces the next topic on building artificial neural networks using the Pytorch library.

What steps are involved in performing cross validation according to the text?

What was the average accuracy measure mentioned in the text?