Description
The "Painter By Numbers" Kaggle competition challenged participants to come up with an algorithm which can examine a pair of paintings and make a prediction: are these two paintings by the same artist? This talk explores some of the technical challenges involved with making an exciting and challenging competition, including how to build a simple RandomForestClassifier algorithm with sklearn as well as how to reduce the problem space to a manageable size by using painting metadata and clustering algorithms to identify groups of similar paintings.
I also explain how to implement siamese neural networks in keras and examine how they allow the development of classification algorithms that can learn to extrapolate to new classes. Finally, I explore how the winning algorithm performed in an actual case of art fraud.