  Conventional wisdom states that deep linear neural networks benefit from
expressiveness and optimization advantages over a single linear layer. This
paper suggests that, in practice, the training process of deep linear
fully-connected networks using conventional optimizers is convex in the same
manner as a single linear fully-connected layer. This paper aims to explain
this claim and demonstrate it. Even though convolutional networks are not
aligned with this description, this work aims to attain a new conceptual
understanding of fully-connected linear networks that might shed light on the
possible constraints of convolutional settings and non-linear architectures.
