Resumen
Deep learning models are based on a combination of neural network architectures, optimization parameters and activation functions. All of them provide exponential combinations whose computational fitness is difficult to pinpoint. The intricate resemblance of the microscopic features that are found in bone surface modifications make their differentiation challenging, and determining a baseline combination of optimizers and activation functions for modeling seems necessary for computational economy. Here, we experiment with combinations of the most resolutive activation functions (relu, swish, and mish) and the most efficient optimizers (stochastic gradient descent (SGD) and Adam) for bone surface modification analysis. We show that despite a wide variability of outcomes, a baseline of relu?SGD is advised for raw bone surface modification data. For imbalanced samples, augmented datasets generated through generative adversarial networks are implemented, resulting in balanced accuracy and an inherent bias regarding mark replication. In summary, although baseline procedures are advised, these do not prevent to overcome Wolpert?s ?no free lunch? theorem and extend it beyond model architectures.