Machine Learning (ML) and Deep Learning (DL) feature distinct characteristics, as subsets within AI. The watermelon model differentiates them since the rind means input features and the flesh processes the algorithm. Its seeds include the predictions that are made for ML. The model digs deeper for DL via multiple layers processing complex data in order to produce subtle outputs.
In the watermelon model, ML can resemble a simpler fruit with fewer seeds, and so it indicates more straightforward processing, while DL is similar to a richer fruit with many seeds, and it reflects its more complex, layered analysis. The analogy aids toward visualizing differences in complexity along with depth between ML and DL
