A thermodynamic approach to deep learning

Neural Networks are an incredibly powerful tool used to solve complex problems. The actual functioning of this tool and its behaviour when applied to different kind of problems is not completely explain though. In this work we study the behaviour of a neural network, used to classify images, through a physical model, based on statistical thermodynamics. We found interesting results regarding the temperature of the different components of the network, that may be exploited in a more efficient training algorithm.