This SpringerBrief describes how to build a rigorous end-to-end Mathematical Framework for Deep Neural networks.
In particular, the authors derive gradient descent algorithms in a unified way for several Neural network structures, including multilayer perceptrons, convolutional Neural networks, Deep autoencoders and recurrent Neural networks..
This SpringerBrief describes how to build a rigorous end-to-end Mathematical Framework for Deep Neural networks