Network Geometry and Overfitting

Overfitting arises when the network learns for too long. For most students, the longer they are trained the more they learn, but artificial neural networks are different. Since networks grow neither bored nor tired, it is a little odd that their performance can begin to degrade if training is excessive. To understand this apparent paradox, we need to consider how a neural network learns. 

The relationship between the boiling point of a material in degrees Kelvin and its molecular weight. 

Overfitting is a potentially serious problem in neural networks. It is tackled in two ways (1) by continually monitoring the quality of training as it occurs using a test set, and (2) by ensuring that the geometry of the network (its size and the way the nodes are connected) is appropriate for the size of the dataset. 

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